From a97f11128666f502965012d58946996f46206d79 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Wed, 8 Nov 2023 06:54:01 +0100
Subject: [PATCH] update book

---
 .../_build/.doctrees/environment.pickle       | Bin 589101 -> 591449 bytes
 .../_build/.doctrees/exercisesweek43.doctree  | Bin 975966 -> 975969 bytes
 .../_build/.doctrees/week45.doctree           | Bin 910077 -> 936515 bytes
 .../_build/html/_images/week45_44_2.png       | Bin 0 -> 17131 bytes
 .../_build/html/_images/week45_44_3.png       | Bin 0 -> 45043 bytes
 .../_build/html/_images/week45_44_4.png       | Bin 0 -> 39374 bytes
 .../html/_sources/exercisesweek43.ipynb       | 243 ++++++-
 .../_build/html/_sources/week45.ipynb         | 449 ++++++++++---
 .../_build/html/exercisesweek43.html          | 144 ++---
 doc/LectureNotes/_build/html/searchindex.js   |   2 +-
 doc/LectureNotes/_build/html/week45.html      | 332 +++++++---
 .../jupyter_execute/exercisesweek43.ipynb     |  70 +-
 .../_build/jupyter_execute/week45.ipynb       | 607 ++++++++++++++----
 .../_build/jupyter_execute/week45.py          | 161 ++++-
 .../_build/jupyter_execute/week45_44_2.png    | Bin 0 -> 17131 bytes
 .../_build/jupyter_execute/week45_44_3.png    | Bin 0 -> 45043 bytes
 .../_build/jupyter_execute/week45_44_4.png    | Bin 0 -> 39374 bytes
 doc/LectureNotes/week45.ipynb                 | 449 ++++++++++---
 18 files changed, 1950 insertions(+), 507 deletions(-)
 create mode 100644 doc/LectureNotes/_build/html/_images/week45_44_2.png
 create mode 100644 doc/LectureNotes/_build/html/_images/week45_44_3.png
 create mode 100644 doc/LectureNotes/_build/html/_images/week45_44_4.png
 create mode 100644 doc/LectureNotes/_build/jupyter_execute/week45_44_2.png
 create mode 100644 doc/LectureNotes/_build/jupyter_execute/week45_44_3.png
 create mode 100644 doc/LectureNotes/_build/jupyter_execute/week45_44_4.png

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 4e511105ab1d07841e7be066f3bf54799669801b..3fb124c8cbe6b7e66ee2be033226ca7a3a22d449 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/exercisesweek43.doctree b/doc/LectureNotes/_build/.doctrees/exercisesweek43.doctree
index 9d91937380c03e959962f5c9d6ac2dd17ea90603..376f2c8507b94f08e7c64abbf53f96b1d7f22ed4 100644
GIT binary patch
delta 3343
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diff --git a/doc/LectureNotes/_build/.doctrees/week45.doctree b/doc/LectureNotes/_build/.doctrees/week45.doctree
index 2d7e32789102b69351d16aefdd2e8d7baa29ed1e..6c0dcfa574eb2539de7d3cc8414de28e5a18d9f0 100644
GIT binary patch
delta 34445
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literal 0
HcmV?d00001

diff --git a/doc/LectureNotes/_build/html/_sources/exercisesweek43.ipynb b/doc/LectureNotes/_build/html/_sources/exercisesweek43.ipynb
index eda98ac3a..028ab5889 100644
--- a/doc/LectureNotes/_build/html/_sources/exercisesweek43.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/exercisesweek43.ipynb
@@ -396,10 +396,249 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "cb396cda",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.25\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  1.0\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  1.0\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.75\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on data set:  0.5\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# import necessary packages\n",
     "import numpy as np\n",
diff --git a/doc/LectureNotes/_build/html/_sources/week45.ipynb b/doc/LectureNotes/_build/html/_sources/week45.ipynb
index 5ce32f797..749491e98 100644
--- a/doc/LectureNotes/_build/html/_sources/week45.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ddffe3be",
+   "id": "31a10e2b",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cde0bbff",
+   "id": "019daa83",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f48c6388",
+   "id": "6ecf1c2b",
    "metadata": {
     "editable": true
    },
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba6964ff",
+   "id": "d80a4db8",
    "metadata": {
     "editable": true
    },
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d810bbf",
+   "id": "1ded5c92",
    "metadata": {
     "editable": true
    },
@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "39f540b5",
+   "id": "6dea0513",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09d24c34",
+   "id": "440bf579",
    "metadata": {
     "editable": true
    },
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c84429f",
+   "id": "683b1da2",
    "metadata": {
     "editable": true
    },
@@ -180,7 +180,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ac8fda39",
+   "id": "38be79e0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9da31b6",
+   "id": "081fbe5c",
    "metadata": {
     "editable": true
    },
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afe2e276",
+   "id": "a9798c07",
    "metadata": {
     "editable": true
    },
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "5707b319",
+   "id": "0a7e4e2e",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f067ea7",
+   "id": "dcd07cb8",
    "metadata": {
     "editable": true
    },
@@ -337,7 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "cfe2ed78",
+   "id": "65061d95",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97a315dc",
+   "id": "50492453",
    "metadata": {
     "editable": true
    },
@@ -378,7 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "9363662a",
+   "id": "08a2ab17",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -423,7 +423,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "298800d9",
+   "id": "5408b7dd",
    "metadata": {
     "editable": true
    },
@@ -448,7 +448,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "9db77f36",
+   "id": "76c3d259",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2872089d",
+   "id": "83903231",
    "metadata": {
     "editable": true
    },
@@ -471,7 +471,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "926cdfd7",
+   "id": "58ccd6c3",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -483,7 +483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bd13984",
+   "id": "adda54cb",
    "metadata": {
     "editable": true
    },
@@ -496,7 +496,327 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55242d6d",
+   "id": "8799c034",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Other ways of presenting a classification problem\n",
+    "\n",
+    "For a binary classifcation matrix, the so-called **confusion matrix**, is often used. It can also be extended to more catgeories/classes as well.\n",
+    "The following quantities are then used\n",
+    "1. positive condition number $P$, which represents the number of real positive cases in the data (output one/true etc)\n",
+    "\n",
+    "2. The condition negative number $N$ which is the number of negative cases (ouput zero/false etc)\n",
+    "\n",
+    "3. The true positive number $TP$ which represents whether a positive test result has been correctly classified (the application of our trained model on a test data set)\n",
+    "\n",
+    "4. The true negative $TN$ number which represents whether a negative test has been correctly classified\n",
+    "\n",
+    "5. The false positive $FP$ number, a so-called type I error which tells us about the fraction of  positive test result which are wrongly classified\n",
+    "\n",
+    "6. A false negative $FN$ number, a so-called type II error which, should be pretty obvious, indicates if a negative test has been wrongly classified.\n",
+    "\n",
+    "It is is easy to think in terms of illness. You could think of the above as\n",
+    "1. True positive: Sick people correctly identified as sick\n",
+    "\n",
+    "2. False positive: Healthy people incorrectly identified as sick\n",
+    "\n",
+    "3. True negative: Healthy people correctly identified as healthy\n",
+    "\n",
+    "4. False negative: Sick people incorrectly identified as healthy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfbe0571",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Combinations of classification results\n",
+    "\n",
+    "It is common in the literature to define various combinations the above numbers. The most commonly used are\n",
+    "\n",
+    "**Sensitivity, recall, hit rate, or true positive rate $TPR$. It is the probability of a positive test result, conditioned on the individual truly being positive.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f7c63d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {TPR} ={\\frac {\\mathrm {TP} }{\\mathrm {P} }}={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FN} }}=1-\\mathrm {FNR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e674d10",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The $TPR$ defines how many correct positive results occur among all positive samples available during the test\n",
+    "\n",
+    "**Miss rate or false negative rate $FNR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65577837",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FNR} ={\\frac {\\mathrm {FN} }{\\mathrm {P} }}={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TP} }} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5e8802d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**Specificity, selectivity or true negative rate $TNR$. It is the probability of a negative test result, conditioned on the individual truly being negative.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07c61591",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {TNR} ={\\frac {\\mathrm {TN} }{\\mathrm {N} }}={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FP} }}=1-\\mathrm {FPR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3f61bb3",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "with the fall-out false positive rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6544105e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FPR} ={\\frac {\\mathrm {FP} }{\\mathrm {N} }}={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TN} }}=1-\\mathrm {TNR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b571e85",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The $FPR$ defines how many incorrect positive results occur among\n",
+    "all negative samples available during the test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17d6872a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Positive and negative prediction values\n",
+    "\n",
+    "The positive and negative predictive values \n",
+    "are the proportions of positive and negative results in statistics and\n",
+    "diagnostic tests that are true positive and true negative results,\n",
+    "respectively.[1] The PPV and NPV describe the performance of a\n",
+    "diagnostic test or other statistical measure. A high result can be\n",
+    "interpreted as indicating the accuracy of such a statistic.\n",
+    "\n",
+    "**Precision or positive predictive value $PPV$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "122f7e6c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {PPV} ={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FP} }}=1-\\mathrm {FDR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f44a5e78",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**Negative predictive value $NPV$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86fa9670",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {NPV} ={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FN} }}=1-\\mathrm {FOR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a30e358d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Other quantities\n",
+    "\n",
+    "**False discovery rate $FDR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f0de6de",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FDR} ={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TP} }}=1-\\mathrm {PPV} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4cc93f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**False omission rate $FOR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4866ec0a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FOR} ={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TN} }}=1-\\mathrm {NPV} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1c8c45",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## $F_1$ score\n",
+    "\n",
+    "In statistical analysis of binary classification, the F-score or\n",
+    "F-measure is a measure of a test's accuracy. It is calculated from the\n",
+    "precision and recall of the test, where the precision is the number of\n",
+    "true positive results divided by the number of all positive results,\n",
+    "including those not identified correctly, and the recall is the number\n",
+    "of true positive results divided by the number of all samples that\n",
+    "should have been identified as positive. Precision is also known as\n",
+    "positive predictive value, and recall is also known as sensitivity in\n",
+    "diagnostic binary classification.\n",
+    "\n",
+    "The F1 score is the harmonic mean of the precision and recall. It thus\n",
+    "symmetrically represents both precision and recall in one metric.  The\n",
+    "highest possible value of an F-score is 1.0, indicating perfect\n",
+    "precision and recall, and the lowest possible value is 0, if either\n",
+    "precision or recall are zero.\n",
+    "\n",
+    "It is defined as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e651a1f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {F} _{1}=2\\times {\\frac {\\mathrm {PPV} \\times \\mathrm {TPR} }{\\mathrm {PPV} +\\mathrm {TPR} }}={\\frac {2\\mathrm {TP} }{2\\mathrm {TP} +\\mathrm {FP} +\\mathrm {FN} }}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d83efe41",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## ROC curve\n",
+    "\n",
+    "A receiver operating characteristic curve, or ROC curve, is a\n",
+    "graphical plot that illustrates the performance of a binary classifier\n",
+    "model at varying threshold values.\n",
+    "\n",
+    "The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.\n",
+    "\n",
+    "To draw a ROC curve, only the true positive rate (TPR) and false\n",
+    "positive rate (FPR) are needed (as functions of some classifier\n",
+    "parameter). The TPR defines how many correct positive results occur\n",
+    "among all positive samples available during the test. FPR, on the\n",
+    "other hand, defines how many incorrect positive results occur among\n",
+    "all negative samples available during the test.\n",
+    "\n",
+    "See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d088215b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Cumulative gain curve\n",
+    "\n",
+    "The cumulative gain curve is a performance evaluation used typically for binary classification problems.\n",
+    "It plots the $TPR$ True Positive Rate or Sensitivity (which represents the \n",
+    "fraction of examples correctly classified\n",
+    "against Predictive Positive Rate, which represents \n",
+    "the fraction of positively predicted examples.\n",
+    "\n",
+    "The examples below show the confusion matrix, the ROC curve and the cumulative gain for the Wisconsin cancer data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c0bcd1f",
    "metadata": {
     "editable": true
    },
@@ -507,7 +827,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "00c15782",
+   "id": "2a6f80df",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -550,40 +870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7a8eeb7",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## ROC curve\n",
-    "\n",
-    "A receiver operating characteristic curve, or ROC curve, is a\n",
-    "graphical plot that illustrates the performance of a binary classifier\n",
-    "model at varying threshold values.\n",
-    "\n",
-    "The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.\n",
-    "See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6414e652",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Cumulative gain curve\n",
-    "\n",
-    "The cumulative gain curve is a performance evaluation used typically for binary classification problems.\n",
-    "It plots the $TPR$ True Positive Rate or Sensitivity (which represents the \n",
-    "fraction of examples correctly classified\n",
-    "against Predictive Positive Rate, which represents \n",
-    "the fraction of positively predicted examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7646c025",
+   "id": "58e5b521",
    "metadata": {
     "editable": true
    },
@@ -593,7 +880,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7b4b23b",
+   "id": "4ebe5a91",
    "metadata": {
     "editable": true
    },
@@ -620,7 +907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "741bf972",
+   "id": "182f425e",
    "metadata": {
     "editable": true
    },
@@ -631,7 +918,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f9060416",
+   "id": "8b3ca785",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -704,7 +991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0342b0b",
+   "id": "b0c12b4c",
    "metadata": {
     "editable": true
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@@ -724,7 +1011,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58fdc710",
+   "id": "d81020f6",
    "metadata": {
     "editable": true
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@@ -740,7 +1027,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f793c129",
+   "id": "2d7453c9",
    "metadata": {
     "editable": true
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@@ -762,7 +1049,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7446349",
+   "id": "fc4a082e",
    "metadata": {
     "editable": true
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@@ -780,7 +1067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fac85e06",
+   "id": "c1106ad9",
    "metadata": {
     "editable": true
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@@ -826,7 +1113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e3fbdb7",
+   "id": "b139ef7b",
    "metadata": {
     "editable": true
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@@ -847,7 +1134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f29008fb",
+   "id": "70c95078",
    "metadata": {
     "editable": true
    },
@@ -908,7 +1195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dedfb5dd",
+   "id": "2ca24031",
    "metadata": {
     "editable": true
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@@ -933,7 +1220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2abfa20e",
+   "id": "93648979",
    "metadata": {
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@@ -952,7 +1239,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abc4dabb",
+   "id": "b8806dcd",
    "metadata": {
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@@ -976,7 +1263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67e1ca86",
+   "id": "b0edad40",
    "metadata": {
     "editable": true
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@@ -1058,7 +1345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc04ebe9",
+   "id": "58fd18f9",
    "metadata": {
     "editable": true
    },
@@ -1074,7 +1361,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "a5882cbb",
+   "id": "a604caa5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1113,7 +1400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18221ec4",
+   "id": "545336c9",
    "metadata": {
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    },
@@ -1157,7 +1444,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "d4982f3a",
+   "id": "3ef3ed13",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1240,7 +1527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a146c2c5",
+   "id": "8a8b29b8",
    "metadata": {
     "editable": true
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@@ -1251,7 +1538,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "2f7691d6",
+   "id": "d840beea",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1353,7 +1640,7 @@
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   {
    "cell_type": "markdown",
-   "id": "618a775c",
+   "id": "2e5b339f",
    "metadata": {
     "editable": true
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@@ -1375,7 +1662,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "5f955304",
+   "id": "370e799d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1472,7 +1759,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae8bb1a0",
+   "id": "d8f62fc4",
    "metadata": {
     "editable": true
    },
@@ -1498,7 +1785,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "701451d4",
+   "id": "4ea36ea9",
    "metadata": {
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     "editable": true
diff --git a/doc/LectureNotes/_build/html/exercisesweek43.html b/doc/LectureNotes/_build/html/exercisesweek43.html
index a03b643f7..b1030881b 100644
--- a/doc/LectureNotes/_build/html/exercisesweek43.html
+++ b/doc/LectureNotes/_build/html/exercisesweek43.html
@@ -1035,74 +1035,6 @@ Accuracy score on data set:  1.0
 Learning rate  =  0.1
 Lambda =  0.01
 Accuracy score on data set:  1.0
-
-Learning rate  =  0.1
-Lambda =  0.1
-Accuracy score on data set:  0.5
-
-Learning rate  =  0.1
-Lambda =  1.0
-Accuracy score on data set:  0.5
-
-Learning rate  =  0.1
-Lambda =  10.0
-Accuracy score on data set:  0.5
-
-Learning rate  =  1.0
-Lambda =  1e-05
-Accuracy score on data set:  0.75
-
-Learning rate  =  1.0
-Lambda =  0.0001
-Accuracy score on data set:  0.75
-
-Learning rate  =  1.0
-Lambda =  0.001
-Accuracy score on data set:  0.75
-
-Learning rate  =  1.0
-Lambda =  0.01
-Accuracy score on data set:  0.5
-
-Learning rate  =  1.0
-Lambda =  0.1
-Accuracy score on data set:  0.5
-
-Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on data set:  0.5
-
-Learning rate  =  1.0
-Lambda =  10.0
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  0.0001
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  0.001
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  0.01
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  0.1
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  1.0
-Accuracy score on data set:  0.5
-
-Learning rate  =  10.0
-Lambda =  10.0
-Accuracy score on data set:  0.5
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
@@ -1127,7 +1059,76 @@ Accuracy score on data set:  0.5
   warnings.warn(
 </pre></div>
 </div>
-<img alt="_images/exercisesweek43_26_2.png" src="_images/exercisesweek43_26_2.png" />
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate  =  0.1
+Lambda =  0.1
+Accuracy score on data set:  0.5
+
+Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on data set:  0.75
+
+Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on data set:  0.75
+
+Learning rate  =  1.0
+Lambda =  0.001
+Accuracy score on data set:  0.75
+
+Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on data set:  0.5
+
+Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on data set:  0.5
+
+Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  1.0
+Lambda =  10.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on data set:  0.5
+
+Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on data set:  0.5
+</pre></div>
+</div>
+<img alt="_images/exercisesweek43_26_3.png" src="_images/exercisesweek43_26_3.png" />
 </div>
 </div>
 </div>
@@ -5432,9 +5433,8 @@ case.</p>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.001, Lambda=0
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
+
+  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  [----------------------------------------] 0.1000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 8014fab54..f5c462620 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural Networks"],titleterms:{"1":[0,15,16,17,18,26,32,33],"12":39,"13":27,"14":35,"19":40,"2":[0,15,16,17,18,27,32,33,42],"2023":[26,30],"21":36,"23":36,"26":41,"3":[0,15,16,32,33],"31":33,"34":[15,32],"35":[16,33],"36":[17,34],"37":[18,35],"38":[19,36],"39":[20,21,37],"3d":42,"4":[0,33],"40":[21,38],"41":[21,39],"42":[22,40],"43":[23,41],"44":[23,41,42],"45":43,"5":0,"7":34,"9":[26,43],"case":[8,10,29,33,34,36,37],"class":36,"do":[1,32,34,35,37,38,39,40,41],"final":[12,21,27,33,34,37,38,39,41,42],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,29,32,33,34,35,36,37,38,39,40,41,42],"import":[5,21,25,32,33,34,42],"long":43,"new":[4,34,35,43],"short":43,"try":43,A:[0,1,4,8,9,21,32,34,35,36,40,41,42,43],AND:[23,38,39,40,41],And:[21,32,33,34,36,37,38],But:[21,37,38],In:30,Is:[40,41],Ising:6,No:42,OR:[38,39,40,41],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,32,33,34,35,36,37,38,39,40,41,42,43],To:[32,33],With:[4,34,43],about:[32,33],abov:[23,34,41,42],activ:[1,12,23,27,34,38,39,40,41,42,43],ad:[0,6,17,26,32,33,38,39],adaboost:10,adagrad:[13,21,37,38],adam:[13,21,37,38],adapt:[10,21,37,38],add:42,addit:[42,43],adjust:[1,39,40,41],advanc:21,adversari:4,again:[3,9,36,42,43],ai:32,aim:[8,9,17,18,19,20,21,22,23,32],aka:[32,33],al:21,algebra:[25,32],algorithm:[9,10,11,12,21,27,33,37,38,39,40],algortithm:[13,36,37],all:[8,43],an:[0,4,10,32,43],analys:[5,33],analysi:[0,5,6,11,24,26,27,29,32,33,34,35],analyt:[0,16,17,21,41],ani:[13,36,37],anoth:[9,34,35],appli:24,approach:[0,8,14,32,35,37,38],approxim:[12,39],architectur:[1,39,40,41],argument:[37,38],arithmet:42,arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[2,13,21,37,38,41],automat:[13,21,37,38,41],avoid:[37,38],b:[17,26,27,37,38],back:[1,11,12,39,40,41],background:[24,26,27,35],backpropag:[42,43],backward:43,bag:10,base:[13,21,35,37,38],basic:[0,5,7,9,10,11,25,33,34,35,36,43],batch:[1,37,38,40,41],bay:[5,34,35],befor:11,beta:[34,35],better:[8,38,39],bia:[6,26,35],bias:[39,40,41],binari:[1,39,40],bind:32,bird:10,boldsymbol:[33,34,35],boost:10,bootstrap:[6,10,35],boston:[0,33],breast:[1,40,41],brief:[32,35,36,37,42],bring:[12,39],build:[1,3,9,23,40,41,42],c:[26,27,32],calcul:33,can:[21,32,35,37,38,43],cancer:[1,7,9,11,36,40,41,43],cart:9,cell:43,center:33,central:[13,24,29,35,36,37],chain:[12,39],challeng:36,chang:10,channel:32,chi:[0,32],choic:[40,41],choos:[1,39,40,41],cifar01:[3,42],classic:11,classif:[1,9,10,23,27,36,39,40,41,43],classifi:[8,36],clip:[1,40,41],cluster:14,cnn:[3,42],code:[0,1,2,5,9,11,12,13,14,21,23,27,32,33,34,35,36,37,38,39,40,41,42],coeffici:42,collect:[1,3,39,40,41,42],come:36,commun:32,compact:36,compar:[2,10,41],comparison:34,compet:[21,37,38],compil:42,complet:33,complex:[0,6,26,33],complic:[6,37,38],compon:11,comput:[9,37,38],computation:35,computerlab:32,con:9,concept:29,condit:[34,35,36,37],confid:35,conjug:[13,37],connect:42,construct:[39,40,41],content:42,continu:38,contn:32,convex:[8,13,36,37],convolut:[3,12,38,39,42],convolution2dlay:42,correctli:[34,35],correl:[11,33,36,42,43],correspond:[36,37],cost:[1,10,23,33,34,35,36,37,39,40,41,42],cours:[24,31,32],covari:[5,11,29,33],cover:32,critic:27,cross:[6,26,35,36],ct:42,cumul:43,curv:43,cython:32,d:[26,27],data:[0,1,3,6,7,9,11,15,16,23,24,26,29,32,33,34,36,39,40,41,42,43],dataset:[1,3,39,40,41,42],deadlin:[26,27,32],decai:[2,37,38,41],decis:[9,10],decomposit:[5,11,17,25,33],deep:[1,2,36,40,41],defin:[1,32,39,40,41],definit:39,degre:[0,33],deliveri:[26,27],delta:35,demonstr:42,dens:[0,32,42],deriv:[5,12,33,34,35,36,37,39,40,41],descent:[2,10,13,21,27,36,37,38,41],descript:26,design:33,detail:[3,32,41,42],develop:[1,39,40,41],diagon:11,differ:[8,27,37,38,42],different:21,differenti:[2,13,37,38,41],diffus:[2,41],dimension:[2,3,8,23,26,33,41,42],directli:[37,38],disadvantag:9,discret:29,discuss:[36,43],distribut:[5,29,34,35],distrubut:[34,35],doe:[33,34,38,39],domain:[29,42],don:42,dot:[37,38],down:[1,40,41],dropout:[1,40,41],e:[26,27],each:36,economi:33,effect:43,effici:42,electron:[26,27],element:[0,29,32,37,38],elimin:25,energi:32,ensembl:10,entropi:[9,36],environ:[0,15,32],equat:[0,2,12,32,33,34,36,37,39,41],error:[0,10,32,33,35],essenti:32,estim:[34,35],et:21,etc:[32,42],euler:[2,41],evalu:[1,27,39,40,41,42],exampl:[0,1,2,3,4,6,7,8,9,10,21,32,33,34,35,36,37,38,39,40,41,42,43],exercis:[0,6,15,16,17,18,19,20,21,22,23,32,33,35,41],expect:[18,29,34,35],expens:35,experi:29,explod:43,explor:[0,15,16,32],exponenti:[2,41],express:[17,18,32,33,36,37],extend:[36,37],extrapol:[4,43],extrem:[10,32],ey:10,f:[26,27],fall:30,famili:[1,32,33,40,41],famou:25,fantast:33,featur:[9,25,33],feed:[1,12,38,39,40,41],file:42,find:[35,37,38,42],fine:[1,40,41],first:[4,12,27,32,33,34,36,37,39,41],fit:[0,10,32,34],fix:33,flatten:42,flow:40,fold:[35,36],forc:[3,42],forest:10,format:[26,27,32,43],forward:[1,2,12,38,39,40,41],four:43,fourier:[3,42],frank:[6,26,33],freedom:[0,33],frequent:33,frequentist:[0,32],fridai:36,from:[5,10,12,21,27,33,34,35,36,37,38,39,42],full:[2,39,40,41,42],fulli: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36,37,38,39,40,41,43],zeros_lik:4,zeroth:33,zfill:4,zip:[2,4,6,13,38],zm_h:[0,32],zmq:[],zn:[0,33],zone:[0,33],zoom:32,zx:[25,32],zy:[25,32],zz:[25,32]},titles:["<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Exercises weeks 43 and 44","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Project 2 on Machine Learning, deadline November 13 (Midnight)","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44,  Convolutional Neural Networks (CNN)","Week 45,  Recurrent Neural Networks"],titleterms:{"1":[0,15,16,17,18,26,32,33],"12":39,"13":27,"14":35,"19":40,"2":[0,15,16,17,18,27,32,33,42],"2023":[26,30],"21":36,"23":36,"26":41,"3":[0,15,16,32,33],"31":33,"34":[15,32],"35":[16,33],"36":[17,34],"37":[18,35],"38":[19,36],"39":[20,21,37],"3d":42,"4":[0,33],"40":[21,38],"41":[21,39],"42":[22,40],"43":[23,41],"44":[23,41,42],"45":43,"5":0,"7":34,"9":[26,43],"case":[8,10,29,33,34,36,37],"class":36,"do":[1,32,34,35,37,38,39,40,41],"final":[12,21,27,33,34,37,38,39,41,42],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,29,32,33,34,35,36,37,38,39,40,41,42],"import":[5,21,25,32,33,34,42],"long":43,"new":[4,34,35,43],"short":43,"try":43,A:[0,1,4,8,9,21,32,34,35,36,40,41,42,43],AND:[23,38,39,40,41],And:[21,32,33,34,36,37,38],But:[21,37,38],In:30,Is:[40,41],Ising:6,No:42,OR:[38,39,40,41],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,32,33,34,35,36,37,38,39,40,41,42,43],To:[32,33],With:[4,34,43],about:[32,33],abov:[23,34,41,42],activ:[1,12,23,27,34,38,39,40,41,42,43],ad:[0,6,17,26,32,33,38,39],adaboost:10,adagrad:[13,21,37,38],adam:[13,21,37,38],adapt:[10,21,37,38],add:42,addit:[42,43],adjust:[1,39,40,41],advanc:21,adversari:4,again:[3,9,36,42,43],ai:32,aim:[8,9,17,18,19,20,21,22,23,32],aka:[32,33],al:21,algebra:[25,32],algorithm:[9,10,11,12,21,27,33,37,38,39,40],algortithm:[13,36,37],all:[8,43],an:[0,4,10,32,43],analys:[5,33],analysi:[0,5,6,11,24,26,27,29,32,33,34,35],analyt:[0,16,17,21,41],ani:[13,36,37],anoth:[9,34,35],appli:24,approach:[0,8,14,32,35,37,38],approxim:[12,39],architectur:[1,39,40,41],argument:[37,38],arithmet:42,arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[2,13,21,37,38,41],automat:[13,21,37,38,41],avoid:[37,38],b:[17,26,27,37,38],back:[1,11,12,39,40,41],background:[24,26,27,35],backpropag:[42,43],backward:43,bag:10,base:[13,21,35,37,38],basic:[0,5,7,9,10,11,25,33,34,35,36,43],batch:[1,37,38,40,41],bay:[5,34,35],befor:11,beta:[34,35],better:[8,38,39],bia:[6,26,35],bias:[39,40,41],binari:[1,39,40],bind:32,bird:10,boldsymbol:[33,34,35],boost:10,bootstrap:[6,10,35],boston:[0,33],breast:[1,40,41],brief:[32,35,36,37,42],bring:[12,39],build:[1,3,9,23,40,41,42],c:[26,27,32],calcul:33,can:[21,32,35,37,38,43],cancer:[1,7,9,11,36,40,41,43],cart:9,cell:43,center:33,central:[13,24,29,35,36,37],chain:[12,39],challeng:36,chang:10,channel:32,chi:[0,32],choic:[40,41],choos:[1,39,40,41],cifar01:[3,42],classic:11,classif:[1,9,10,23,27,36,39,40,41,43],classifi:[8,36],clip:[1,40,41],cluster:14,cnn:[3,42],code:[0,1,2,5,9,11,12,13,14,21,23,27,32,33,34,35,36,37,38,39,40,41,42],coeffici:42,collect:[1,3,39,40,41,42],combin:43,come:36,commun:32,compact:36,compar:[2,10,41],comparison:34,compet:[21,37,38],compil:42,complet:33,complex:[0,6,26,33],complic:[6,37,38],compon:11,comput:[9,37,38],computation:35,computerlab:32,con:9,concept:29,condit:[34,35,36,37],confid:35,conjug:[13,37],connect:42,construct:[39,40,41],content:42,continu:38,contn:32,convex:[8,13,36,37],convolut:[3,12,38,39,42],convolution2dlay:42,correctli:[34,35],correl:[11,33,36,42,43],correspond:[36,37],cost:[1,10,23,33,34,35,36,37,39,40,41,42],cours:[24,31,32],covari:[5,11,29,33],cover:32,critic:27,cross:[6,26,35,36],ct:42,cumul:43,curv:43,cython:32,d:[26,27],data:[0,1,3,6,7,9,11,15,16,23,24,26,29,32,33,34,36,39,40,41,42,43],dataset:[1,3,39,40,41,42],deadlin:[26,27,32],decai:[2,37,38,41],decis:[9,10],decomposit:[5,11,17,25,33],deep:[1,2,36,40,41],defin:[1,32,39,40,41],definit:39,degre:[0,33],deliveri:[26,27],delta:35,demonstr:42,dens:[0,32,42],deriv:[5,12,33,34,35,36,37,39,40,41],descent:[2,10,13,21,27,36,37,38,41],descript:26,design:33,detail:[3,32,41,42],develop:[1,39,40,41],diagon:11,differ:[8,27,37,38,42],different:21,differenti:[2,13,37,38,41],diffus:[2,41],dimension:[2,3,8,23,26,33,41,42],directli:[37,38],disadvantag:9,discret:29,discuss:[36,43],distribut:[5,29,34,35],distrubut:[34,35],doe:[33,34,38,39],domain:[29,42],don:42,dot:[37,38],down:[1,40,41],dropout:[1,40,41],e:[26,27],each:36,economi:33,effect:43,effici:42,electron:[26,27],element:[0,29,32,37,38],elimin:25,energi:32,ensembl:10,entropi:[9,36],environ:[0,15,32],equat:[0,2,12,32,33,34,36,37,39,41],error:[0,10,32,33,35],essenti:32,estim:[34,35],et:21,etc:[32,42],euler:[2,41],evalu:[1,27,39,40,41,42],exampl:[0,1,2,3,4,6,7,8,9,10,21,32,33,34,35,36,37,38,39,40,41,42,43],exercis:[0,6,15,16,17,18,19,20,21,22,23,32,33,35,41],expect:[18,29,34,35],expens:35,experi:29,explod:43,explor:[0,15,16,32],exponenti:[2,41],express:[17,18,32,33,36,37],extend:[36,37],extrapol:[4,43],extrem:[10,32],ey:10,f:[26,27],f_1:43,fall:30,famili:[1,32,33,40,41],famou:25,fantast:33,featur:[9,25,33],feed:[1,12,38,39,40,41],file:42,find:[35,37,38,42],fine:[1,40,41],first:[4,12,27,32,33,34,36,37,39,41],fit:[0,10,32,34],fix:33,flatten:42,flow:40,fold:[35,36],forc:[3,42],forest:10,format:[26,27,32,43],forward:[1,2,12,38,39,40,41],four:43,fourier:[3,42],frank:[6,26,33],freedom:[0,33],frequent:33,frequentist:[0,32],fridai:36,from:[5,10,12,21,27,33,34,35,36,37,38,39,42],full:[2,39,40,41,42],fulli:42,funtion:[40,41],further:[3,5,33,42],g:26,gain:43,gan:4,gate:[23,38,39,40,41],gaussian:25,gd:[13,21,37,38],gener:[4,9,32],geometr:[11,36,37],get:21,gini:9,good:[0,32],goodfellow:21,grade:[30,32],gradient:[1,2,10,13,21,27,36,37,38,40,41,43],grid:[36,43],group:36,growth:[2,41],ha:24,half:42,hand:[23,41],handl:[25,32,33],happen:[34,35],have:32,heard:32,hessian:[33,36,37],hidden:[2,40,41,42,43],histogram:35,homework:[36,37],hous:[0,33],how:36,hyperbol:[38,39],hyperparamet:[1,39,40,41],hyperplan:8,i:[1,40,41],id3:9,idea:[11,42],ideal:[36,37],ident:[34,35],identifi:35,ii:[32,41],iid:[34,35],iii:41,illustr:[34,38,39],imag:42,implement:[1,39,40,41,43],implic:[5,33],improv:[1,37,39,40,41],includ:[13,21,36,37,38],increment:11,independ:[34,35],index:9,inform:30,initi:43,input:[2,41,43],instal:[24,26,32],instructor:30,intercept:33,interpret:[5,11,32,33,34,35,36,37],interv:35,introduc:[11,13,21,33,37,38],introduct:[0,6,24,25,26,27,32,38,39,40],invers:[5,25,34],invert:33,iter:[10,37],its:33,iv:41,jacobian:[33,41],jax:[13,21,37,38],job:[38,39],julia:32,jungl:10,k:[35,36],kei:42,kera:[1,3,40,41,42],kernel:[8,11,42],l:39,lab:43,lagrangian:8,lambda:[36,43],lasso:[5,6,26,33,34,35,36],last:[33,35,36,38],later:[5,33],layer:[1,2,3,12,39,40,41,42],layout:43,learn:[0,1,2,11,13,14,15,16,21,23,24,26,27,32,33,34,35,36,37,38,39,40,41,43],least:[5,6,18,26,32,33,34],lectur:[32,33,34,35,36,39,40,41,42,43],level:10,librari:[24,32],likelihood:[7,34,35,36],limit:[1,13,29,35,36,37,38,40,41],linear:[0,8,13,25,27,32,33,34,36,43],link:[5,11,28,31,33,34,35],list:42,literatur:[26,27],logist:[7,27,32,36,37,38,39,40,41],loop:[37,38],loss:[33,36,37],lstm:43,lu:25,machin:[0,8,13,24,26,27,32,36,37],made:[34,35],mai:32,main:29,make:[0,9,10,15,16,32,33],mani:[10,12,39],manipul:33,margin:[34,35],mass:32,materi:[28,32,33,34,35,36,41,42,43],math:[5,33],mathemat:[3,5,8,33,37,38,39,42],matric:[5,25,32,34],matrix:[1,5,11,12,25,32,33,34,36,37,38,39,40,41,43],matter:[0,32],max:33,maximum:[34,35,36],mean:[0,32,33,34,36],measur:[36,43],medic:42,meet:[5,10,29,32,33],memori:43,mercer:8,method:[6,9,10,13,21,23,26,35,36,37,38,41],midnight:[26,27],min:33,mini:[37,38],minibatch:[21,37,38],minim:[32,36,41],ml:32,mle:[34,35],mlp:[12,39],mnist:[3,4,42],model:[0,1,4,6,12,32,38,39,40,41,42],moment:[37,38],momentum:[13,21,37,38],moon:[8,9],more:[3,6,21,25,26,32,33,34,35,36,37,38,40,41,42],multi:[38,39,40],multiclass:[23,41],multilay:[12,38,39],multipl:[1,3,39,40,41,42],multipli:8,need:[26,32,43],neg:43,network:[1,2,3,4,7,12,23,27,32,36,38,39,40,41,42,43],neural:[1,2,3,4,7,12,23,27,32,38,39,40,41,42,43],neuron:[38,39,42],newton:[21,36,37,38],nice:[40,41],nn:42,noen:[21,37,38],non:8,normal:[0,1,35,40,41],notat:[12,38,39],note:[21,33,34,35],novemb:[27,42,43],now:[1,9,13,21,34,35,37,38,40,41],nuclear:[0,32],nueral:36,numba:32,number:[0,2,21,29,33,37,38,41],numer:[2,26,27,29,41],numpi:[21,25,32,37,38],object:[3,39,40,41,42],obtain:11,octob:[26,39,40,41],od:[2,41],off:[6,26],ol:[5,6,21,26,34,35,37,38],one:[2,12,36,37,39,41],ones:[38,39],oper:25,optim:[1,8,13,24,32,33,36,37,38,39,40,41,42,43],order:[13,21,37,38],ordinari:[5,6,18,26,32,33,34,41],organ:[0,32],orient:[39,40,41],oslo:31,other:[4,9,11,12,23,25,32,33,36,38,39,41,43],our:[0,4,5,11,13,32,33,36,37,39,40,41,42],outcom:[24,32],output:[2,40,41,43],overarch:[0,4,8,9,17,18,19,20,21,22,23,32,33,43],overview:[10,32,37,38],own:[0,10,11,15,16,27,32,33,42],packag:[25,32],pad:42,panda:[32,33],paper:26,paramet:[32,33,36,37,38,43],part:[13,24,26,27,33,36,37,42],partial:[2,41],pass:[1,39,40,41,43],pca:11,pdf:29,pencil:26,perceptron:[12,38,39,40],perform:[1,9,39,40,41],period:[3,42],perspect:[1,40,41],plan:[33,34,35,36,37,38,39,40,41,42,43],plot:[35,36],point:[4,43],poisson:[2,41],polynomi:[3,34,42],pool:42,popul:[2,41],posit:43,possibl:41,practic:[13,21,30,32,37,38,43],pre:[1,3,39,40,41,42],predict:[4,43],predictor:36,preprocess:33,prerequisit:[3,24,32,42],present:43,princip:11,principl:[3,42],pro:9,probabl:[5,29,34,35],problem:[1,2,13,21,32,33,34,36,37,38,39,40,41,43],procedur:[9,32],process:[1,3,39,40,41,42],product:[37,38],program:[2,13,26,27,36,37,41],project:[6,26,27,32,36],prop:[13,37,38],propag:[1,12,39,40,41],properti:[5,29,33,36],python:[0,9,15,24,25,32],quantiti:43,quick:8,r:32,random:[10,11,29,36,43],raphson:[36,37],rate:[21,23,37,38,41],read:[9,32,33,35],real:[6,26,32],recogn:42,recommend:[32,33,37,38],rectangular:34,recurr:[4,12,38,39,43],recurs:[37,38],reduc:[0,33],reduct:[3,42],reformul:[2,41],regress:[0,5,6,7,9,10,13,17,18,26,27,32,33,34,35,36,37,38,39],regular:[1,36,39,40,41,42,43],relat:33,relev:[31,33,35,36,38,39],relu:[1,40,41],remark:[3,42],remind:[6,8,32,36,37],repeat:33,replac:[13,37,38],report:[26,27],repositori:35,repres:[23,39,40,41],requir:[2,24,41],resampl:[6,26,34,35],rescal:[6,34],residu:33,resourc:[2,41],result:[33,34,43],review:40,revisit:[13,36,37],rewrit:[32,33,35],rewritten:36,ridg:[0,5,6,17,18,26,33,34,35,36,37],rm:[13,37,38],rmsprop:[21,37,38],rnn:43,roc:43,rule:[12,39],run:42,s:[8,10,21,35,36,37,38],same:[13,21,35,37,38,42],sampl:11,scale:[33,35,42],scan:42,schedul:[23,28,32,41,42],schemat:9,scheme:[2,41],scienc:32,scikit:[0,1,11,15,16,23,32,33,34,35,36,37,39,40,41],score:43,search:[36,43],second:[13,21,37,38],select:36,semest:30,sensit:[36,37],separ:42,septemb:[34,35,36],session:[34,43],set:[0,2,3,9,12,15,23,32,33,36,39,40,41,42],setup:41,sever:43,sgd:[13,37,38],should:[1,40,41],sigmoid:[40,41],similar:[13,21,37,38],simpl:[0,4,9,13,32,33,34,36,37,38,42,43],singl:[10,38,39],singular:[5,11,17,33],size:33,slightli:[37,38],soft:8,softmax:[1,39,40],softwar:[26,32],solut:41,solv:[2,34,36,37,41],solver:13,some:[13,25,32,33,36,37],sound:42,specif:41,specifi:[2,41,43],speed:42,split:[0,15,16,32,33],squar:[0,5,6,10,18,26,32,33,34],standard:[13,33,35,37],start:[21,38],state:[0,32,43],statement:32,statist:[5,6,24,29,32,34,35],steepest:[10,13,36,37],step:[27,35,36,37,38],still:33,stochast:[13,21,27,29,37,38],stop:[37,38],stride:42,strong:42,strongli:32,studi:[36,43],subtract:33,suggest:32,sum:35,summari:[30,32,38],superposit:[3,42],supervis:[1,40,41],support:8,svd:[5,33,34],syntax:[37,38],systemat:[3,42],t:[33,34,42],taken:21,target:43,teach:[28,30],teacher:[30,32],technic:[34,41],techniqu:[6,11,26,34],technolog:24,tensor:40,tensorflow:[1,3,40,41,42],tent:32,term:[35,39,43],test:[0,1,15,16,23,27,32,33,34,39,40,41],textbook:[31,32],than:[36,37],thei:32,theorem:[5,8,11,12,29,34,35,39],theori:29,thi:[17,18,19,20,21,22,32],thing:43,think:33,through:43,thursdai:[33,34,35,36,39,40,41,42,43],time:[37,38,43],tip:[13,21,37,38],togeth:[12,39],tool:32,top:[1,40,41,42],topic:32,toward:11,trade:[6,26],tradeoff:[6,35],train:[0,1,4,15,16,32,33,39,40,41,42,43],transform:[3,42],tree:[9,10],trial:41,tuesdai:34,tune:[1,40,41],two:[3,8,24,26,33,36,42],type:[2,4,12,32,38,39,41,43],uio:32,understand:35,unit:[42,43],univers:[12,31,39],unsupervis:14,unsupport:[37,38],up:[0,2,9,12,15,21,23,32,33,35,36,39,40,41,42],us:[0,1,2,3,7,13,21,23,24,27,32,33,36,37,38,39,40,41,42,43],usag:[23,34,35,41,42],valid:[6,26,35,36],valu:[5,11,17,18,29,33,34,35,36,43],vanish:43,vari:[21,37,38],variabl:[29,36,37],varianc:[6,26,34,35],variou:[0,15,27,32,35,36],vector:[8,12,25,32,33,38,39],veri:[40,41],verifi:42,video:[37,38],view:[0,4,10,33,43],visual:[1,9,39,40,41,42],volum:42,vs:[3,42],wai:[9,21,35,42,43],warm:21,wave:[2,41],we:[21,32,37,38,40,41,43],websit:[40,41],wednesdai:34,week:[15,16,17,18,19,20,21,22,23,32,33,34,35,36,37,38,39,40,41,42,43],weekend:36,weekli:[28,35],weight:[39,40,41],well:42,what:[0,32,33,34,35,42],when:[37,38],which:[1,21,37,38,40,41],why:[32,33,35,38,39,41,42],wisconsin:[7,36,43],word:42,wrap:[33,35,42],write:[4,11,27,34,41],x:[33,34],xgboost:10,xor:[23,38,39,40,41],yet:34,you:32,your:[0,10,15,16,27,32,33],yourself:36,z_j:39,zero:42}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week45.html b/doc/LectureNotes/_build/html/week45.html
index 6785af08a..fdaf51645 100644
--- a/doc/LectureNotes/_build/html/week45.html
+++ b/doc/LectureNotes/_build/html/week45.html
@@ -485,8 +485,31 @@ const thebe_selector_output = ".output, .cell_output"
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#other-measures-in-classification-studies-cancer-data-again">
-   Other measures in classification studies: Cancer Data  again
+  <a class="reference internal nav-link" href="#other-ways-of-presenting-a-classification-problem">
+   Other ways of presenting a classification problem
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#combinations-of-classification-results">
+   Combinations of classification results
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#positive-and-negative-prediction-values">
+   Positive and negative prediction values
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#other-quantities">
+   Other quantities
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#f-1-score">
+   <span class="math notranslate nohighlight">
+    \(F_1\)
+   </span>
+   score
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -499,6 +522,11 @@ const thebe_selector_output = ".output, .cell_output"
    Cumulative gain curve
   </a>
  </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#other-measures-in-classification-studies-cancer-data-again">
+   Other measures in classification studies: Cancer Data  again
+  </a>
+ </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#material-for-lecture-thursday-november-9">
    Material for Lecture Thursday November 9
@@ -664,8 +692,31 @@ const thebe_selector_output = ".output, .cell_output"
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#other-measures-in-classification-studies-cancer-data-again">
-   Other measures in classification studies: Cancer Data  again
+  <a class="reference internal nav-link" href="#other-ways-of-presenting-a-classification-problem">
+   Other ways of presenting a classification problem
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#combinations-of-classification-results">
+   Combinations of classification results
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#positive-and-negative-prediction-values">
+   Positive and negative prediction values
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#other-quantities">
+   Other quantities
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#f-1-score">
+   <span class="math notranslate nohighlight">
+    \(F_1\)
+   </span>
+   score
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -678,6 +729,11 @@ const thebe_selector_output = ".output, .cell_output"
    Cumulative gain curve
   </a>
  </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#other-measures-in-classification-studies-cancer-data-again">
+   Other measures in classification studies: Cancer Data  again
+  </a>
+ </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#material-for-lecture-thursday-november-9">
    Material for Lecture Thursday November 9
@@ -1019,7 +1075,7 @@ case under study.</p>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>RandomizedSearchCV(estimator=Ridge(), n_iter=100,
-                   param_distributions={&#39;alpha&#39;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x105b84cd0&gt;})
+                   param_distributions={&#39;alpha&#39;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x13d4a1640&gt;})
 Best estimated lambda-value: 0.9849967686928113
 MSE score: 1.0853136633465326
 R2 score: -0.0002382102844775691
@@ -1155,6 +1211,130 @@ features are of relevance and which are not. This leads  us to
 the classical Principal Component Analysis (PCA) theorem with
 applications. This will be discussed later this semester (<a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-bs.html">week 43</a>).</p>
 </div>
+<div class="section" id="other-ways-of-presenting-a-classification-problem">
+<h2>Other ways of presenting a classification problem<a class="headerlink" href="#other-ways-of-presenting-a-classification-problem" title="Permalink to this headline">¶</a></h2>
+<p>For a binary classifcation matrix, the so-called <strong>confusion matrix</strong>, is often used. It can also be extended to more catgeories/classes as well.
+The following quantities are then used</p>
+<ol class="simple">
+<li><p>positive condition number <span class="math notranslate nohighlight">\(P\)</span>, which represents the number of real positive cases in the data (output one/true etc)</p></li>
+<li><p>The condition negative number <span class="math notranslate nohighlight">\(N\)</span> which is the number of negative cases (ouput zero/false etc)</p></li>
+<li><p>The true positive number <span class="math notranslate nohighlight">\(TP\)</span> which represents whether a positive test result has been correctly classified (the application of our trained model on a test data set)</p></li>
+<li><p>The true negative <span class="math notranslate nohighlight">\(TN\)</span> number which represents whether a negative test has been correctly classified</p></li>
+<li><p>The false positive <span class="math notranslate nohighlight">\(FP\)</span> number, a so-called type I error which tells us about the fraction of  positive test result which are wrongly classified</p></li>
+<li><p>A false negative <span class="math notranslate nohighlight">\(FN\)</span> number, a so-called type II error which, should be pretty obvious, indicates if a negative test has been wrongly classified.</p></li>
+</ol>
+<p>It is is easy to think in terms of illness. You could think of the above as</p>
+<ol class="simple">
+<li><p>True positive: Sick people correctly identified as sick</p></li>
+<li><p>False positive: Healthy people incorrectly identified as sick</p></li>
+<li><p>True negative: Healthy people correctly identified as healthy</p></li>
+<li><p>False negative: Sick people incorrectly identified as healthy</p></li>
+</ol>
+</div>
+<div class="section" id="combinations-of-classification-results">
+<h2>Combinations of classification results<a class="headerlink" href="#combinations-of-classification-results" title="Permalink to this headline">¶</a></h2>
+<p>It is common in the literature to define various combinations the above numbers. The most commonly used are</p>
+<p><strong>Sensitivity, recall, hit rate, or true positive rate <span class="math notranslate nohighlight">\(TPR\)</span>. It is the probability of a positive test result, conditioned on the individual truly being positive.</strong></p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {TPR} ={\frac {\mathrm {TP} }{\mathrm {P} }}={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FN} }}=1-\mathrm {FNR} }
+\]</div>
+<p>The <span class="math notranslate nohighlight">\(TPR\)</span> defines how many correct positive results occur among all positive samples available during the test</p>
+<p><strong>Miss rate or false negative rate <span class="math notranslate nohighlight">\(FNR\)</span>.</strong></p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {FNR} ={\frac {\mathrm {FN} }{\mathrm {P} }}={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TP} }} }
+\]</div>
+<p><strong>Specificity, selectivity or true negative rate <span class="math notranslate nohighlight">\(TNR\)</span>. It is the probability of a negative test result, conditioned on the individual truly being negative.</strong></p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {TNR} ={\frac {\mathrm {TN} }{\mathrm {N} }}={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FP} }}=1-\mathrm {FPR} }
+\]</div>
+<p>with the fall-out false positive rate</p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {FPR} ={\frac {\mathrm {FP} }{\mathrm {N} }}={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TN} }}=1-\mathrm {TNR} }
+\]</div>
+<p>The <span class="math notranslate nohighlight">\(FPR\)</span> defines how many incorrect positive results occur among
+all negative samples available during the test.</p>
+</div>
+<div class="section" id="positive-and-negative-prediction-values">
+<h2>Positive and negative prediction values<a class="headerlink" href="#positive-and-negative-prediction-values" title="Permalink to this headline">¶</a></h2>
+<p>The positive and negative predictive values
+are the proportions of positive and negative results in statistics and
+diagnostic tests that are true positive and true negative results,
+respectively.[1] The PPV and NPV describe the performance of a
+diagnostic test or other statistical measure. A high result can be
+interpreted as indicating the accuracy of such a statistic.</p>
+<p><strong>Precision or positive predictive value <span class="math notranslate nohighlight">\(PPV\)</span>.</strong></p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {PPV} ={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FP} }}=1-\mathrm {FDR} }
+\]</div>
+<p><strong>Negative predictive value <span class="math notranslate nohighlight">\(NPV\)</span>.</strong></p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {NPV} ={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FN} }}=1-\mathrm {FOR} }
+\]</div>
+</div>
+<div class="section" id="other-quantities">
+<h2>Other quantities<a class="headerlink" href="#other-quantities" title="Permalink to this headline">¶</a></h2>
+<p><strong>False discovery rate <span class="math notranslate nohighlight">\(FDR\)</span>.</strong></p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {FDR} ={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TP} }}=1-\mathrm {PPV} }
+\]</div>
+<p><strong>False omission rate <span class="math notranslate nohighlight">\(FOR\)</span>.</strong></p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {FOR} ={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TN} }}=1-\mathrm {NPV} }
+\]</div>
+</div>
+<div class="section" id="f-1-score">
+<h2><span class="math notranslate nohighlight">\(F_1\)</span> score<a class="headerlink" href="#f-1-score" title="Permalink to this headline">¶</a></h2>
+<p>In statistical analysis of binary classification, the F-score or
+F-measure is a measure of a test’s accuracy. It is calculated from the
+precision and recall of the test, where the precision is the number of
+true positive results divided by the number of all positive results,
+including those not identified correctly, and the recall is the number
+of true positive results divided by the number of all samples that
+should have been identified as positive. Precision is also known as
+positive predictive value, and recall is also known as sensitivity in
+diagnostic binary classification.</p>
+<p>The F1 score is the harmonic mean of the precision and recall. It thus
+symmetrically represents both precision and recall in one metric.  The
+highest possible value of an F-score is 1.0, indicating perfect
+precision and recall, and the lowest possible value is 0, if either
+precision or recall are zero.</p>
+<p>It is defined as</p>
+<div class="math notranslate nohighlight">
+\[
+{\displaystyle \mathrm {F} _{1}=2\times {\frac {\mathrm {PPV} \times \mathrm {TPR} }{\mathrm {PPV} +\mathrm {TPR} }}={\frac {2\mathrm {TP} }{2\mathrm {TP} +\mathrm {FP} +\mathrm {FN} }}}
+\]</div>
+</div>
+<div class="section" id="roc-curve">
+<h2>ROC curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">¶</a></h2>
+<p>A receiver operating characteristic curve, or ROC curve, is a
+graphical plot that illustrates the performance of a binary classifier
+model at varying threshold values.</p>
+<p>The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.</p>
+<p>To draw a ROC curve, only the true positive rate (TPR) and false
+positive rate (FPR) are needed (as functions of some classifier
+parameter). The TPR defines how many correct positive results occur
+among all positive samples available during the test. FPR, on the
+other hand, defines how many incorrect positive results occur among
+all negative samples available during the test.</p>
+<p>See <a class="reference external" href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic">https://en.wikipedia.org/wiki/Receiver_operating_characteristic</a> for more discussions.</p>
+</div>
+<div class="section" id="cumulative-gain-curve">
+<h2>Cumulative gain curve<a class="headerlink" href="#cumulative-gain-curve" title="Permalink to this headline">¶</a></h2>
+<p>The cumulative gain curve is a performance evaluation used typically for binary classification problems.
+It plots the <span class="math notranslate nohighlight">\(TPR\)</span> True Positive Rate or Sensitivity (which represents the
+fraction of examples correctly classified
+against Predictive Positive Rate, which represents
+the fraction of positively predicted examples.</p>
+<p>The examples below show the confusion matrix, the ROC curve and the cumulative gain for the Wisconsin cancer data.</p>
+</div>
 <div class="section" id="other-measures-in-classification-studies-cancer-data-again">
 <h2>Other measures in classification studies: Cancer Data  again<a class="headerlink" href="#other-measures-in-classification-studies-cancer-data-again" title="Permalink to this headline">¶</a></h2>
 <div class="cell docutils container">
@@ -1197,6 +1377,9 @@ applications. This will be discussed later this semester (<a class="reference ex
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(426, 30)
 (143, 30)
+[1.         0.86666667 1.         0.92857143 1.         0.85714286
+ 1.         0.92857143 0.92857143 1.        ]
+Test set accuracy with Logistic Regression: 0.94
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
@@ -1289,33 +1472,12 @@ Please also refer to the documentation for alternative solver options:
   n_iter_i = _check_optimize_result(
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[1.         0.86666667 1.         0.92857143 1.         0.85714286
- 1.         0.92857143 0.92857143 1.        ]
-Test set accuracy with Logistic Regression: 0.94
-</pre></div>
-</div>
-<img alt="_images/week45_22_3.png" src="_images/week45_22_3.png" />
-<img alt="_images/week45_22_4.png" src="_images/week45_22_4.png" />
-<img alt="_images/week45_22_5.png" src="_images/week45_22_5.png" />
+<img alt="_images/week45_44_2.png" src="_images/week45_44_2.png" />
+<img alt="_images/week45_44_3.png" src="_images/week45_44_3.png" />
+<img alt="_images/week45_44_4.png" src="_images/week45_44_4.png" />
 </div>
 </div>
 </div>
-<div class="section" id="roc-curve">
-<h2>ROC curve<a class="headerlink" href="#roc-curve" title="Permalink to this headline">¶</a></h2>
-<p>A receiver operating characteristic curve, or ROC curve, is a
-graphical plot that illustrates the performance of a binary classifier
-model at varying threshold values.</p>
-<p>The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.
-See <a class="reference external" href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic">https://en.wikipedia.org/wiki/Receiver_operating_characteristic</a> for more discussions.</p>
-</div>
-<div class="section" id="cumulative-gain-curve">
-<h2>Cumulative gain curve<a class="headerlink" href="#cumulative-gain-curve" title="Permalink to this headline">¶</a></h2>
-<p>The cumulative gain curve is a performance evaluation used typically for binary classification problems.
-It plots the <span class="math notranslate nohighlight">\(TPR\)</span> True Positive Rate or Sensitivity (which represents the
-fraction of examples correctly classified
-against Predictive Positive Rate, which represents
-the fraction of positively predicted examples.</p>
-</div>
 <div class="section" id="material-for-lecture-thursday-november-9">
 <h2>Material for Lecture Thursday November 9<a class="headerlink" href="#material-for-lecture-thursday-november-9" title="Permalink to this headline">¶</a></h2>
 </div>
@@ -1457,285 +1619,303 @@ systems such as automatic translation and speech-to-text.</p>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 1/100
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-11-07 06:16:59.648382: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-11-08 06:51:57.259901: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 3s - loss: 0.9885 - 3s/epoch - 66ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 3s - loss: 2.5680 - 3s/epoch - 69ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 2/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4316 - 465ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 1.8934 - 563ms/epoch - 11ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 3/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4150 - 494ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 1.4588 - 512ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 4/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4066 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.8688 - 466ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 5/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4037 - 497ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4871 - 457ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 6/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4013 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4162 - 456ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 7/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3980 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4068 - 451ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 8/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3981 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4032 - 453ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 9/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3944 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4009 - 453ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 10/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3951 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3978 - 457ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 11/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3929 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3964 - 451ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 12/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3929 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3936 - 455ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 13/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3915 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3933 - 450ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 14/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3923 - 472ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3938 - 454ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 15/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3901 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3924 - 452ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 16/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 471ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3924 - 456ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 17/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3919 - 454ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 18/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 490ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3907 - 454ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 19/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3888 - 475ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3906 - 453ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 20/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3866 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3907 - 452ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 21/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3875 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3888 - 453ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 22/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3864 - 472ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3898 - 454ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 23/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3883 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3889 - 453ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 24/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3877 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3883 - 452ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 25/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3884 - 491ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3885 - 456ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 26/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3862 - 472ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3886 - 452ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 27/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3844 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3885 - 457ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 28/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3869 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3872 - 454ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 29/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3859 - 470ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3885 - 452ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 30/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3849 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 31/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3861 - 463ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3869 - 454ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 32/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3837 - 462ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3868 - 455ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 33/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3844 - 477ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 34/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3842 - 479ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3872 - 455ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 35/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3825 - 476ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3856 - 481ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 36/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3849 - 477ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3853 - 454ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 37/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3826 - 463ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3859 - 483ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 38/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3816 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3862 - 482ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 39/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3843 - 1s/epoch - 25ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3850 - 508ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 40/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3821 - 522ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3841 - 494ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 41/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3825 - 486ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3848 - 473ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 42/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3822 - 480ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3850 - 479ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 43/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3836 - 521ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3841 - 473ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 44/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3827 - 566ms/epoch - 11ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3847 - 479ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 45/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3815 - 504ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3810 - 463ms/epoch - 9ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 46/100
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3832 - 511ms/epoch - 10ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3843 - 483ms/epoch - 10ms/step
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 47/100
 </pre></div>
 </div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3830 - 467ms/epoch - 9ms/step
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 48/100
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3827 - 485ms/epoch - 10ms/step
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 49/100
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3820 - 479ms/epoch - 10ms/step
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 50/100
+</pre></div>
+</div>
 <div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
 <span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace">                         </span>Traceback (most recent call last)
 <span class="nn">Input In [9],</span> in <span class="ni">&lt;cell line: 53&gt;</span><span class="nt">()</span>
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
index ae9bc9c85..a77175aad 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
@@ -542,7 +542,39 @@
       "Learning rate  =  0.1\n",
       "Lambda =  0.01\n",
       "Accuracy score on data set:  1.0\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.1\n",
       "Lambda =  0.1\n",
       "Accuracy score on data set:  0.5\n",
@@ -613,32 +645,6 @@
       "\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -648,7 +654,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_3.png"
       }
      },
      "output_type": "display_data"
@@ -10681,13 +10687,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Adam: Eta=0.001, Lambda=0\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Adam: Eta=0.001, Lambda=0\n",
       "\r",
       "  [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 "
      ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
index 96057fc92..041dd6334 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ddffe3be",
+   "id": "31a10e2b",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cde0bbff",
+   "id": "019daa83",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f48c6388",
+   "id": "6ecf1c2b",
    "metadata": {
     "editable": true
    },
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba6964ff",
+   "id": "d80a4db8",
    "metadata": {
     "editable": true
    },
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d810bbf",
+   "id": "1ded5c92",
    "metadata": {
     "editable": true
    },
@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "39f540b5",
+   "id": "6dea0513",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09d24c34",
+   "id": "440bf579",
    "metadata": {
     "editable": true
    },
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c84429f",
+   "id": "683b1da2",
    "metadata": {
     "editable": true
    },
@@ -195,7 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ac8fda39",
+   "id": "38be79e0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9da31b6",
+   "id": "081fbe5c",
    "metadata": {
     "editable": true
    },
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afe2e276",
+   "id": "a9798c07",
    "metadata": {
     "editable": true
    },
@@ -297,7 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "5707b319",
+   "id": "0a7e4e2e",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -308,7 +308,7 @@
      "output_type": "stream",
      "text": [
       "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
-      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x105b84cd0>})\n",
+      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x13d4a1640>})\n",
       "Best estimated lambda-value: 0.9849967686928113\n",
       "MSE score: 1.0853136633465326\n",
       "R2 score: -0.0002382102844775691\n"
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f067ea7",
+   "id": "dcd07cb8",
    "metadata": {
     "editable": true
    },
@@ -378,7 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "cfe2ed78",
+   "id": "65061d95",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97a315dc",
+   "id": "50492453",
    "metadata": {
     "editable": true
    },
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "9363662a",
+   "id": "08a2ab17",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "298800d9",
+   "id": "5408b7dd",
    "metadata": {
     "editable": true
    },
@@ -542,7 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "9db77f36",
+   "id": "76c3d259",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -554,7 +554,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2872089d",
+   "id": "83903231",
    "metadata": {
     "editable": true
    },
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "926cdfd7",
+   "id": "58ccd6c3",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bd13984",
+   "id": "adda54cb",
    "metadata": {
     "editable": true
    },
@@ -590,7 +590,327 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55242d6d",
+   "id": "8799c034",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Other ways of presenting a classification problem\n",
+    "\n",
+    "For a binary classifcation matrix, the so-called **confusion matrix**, is often used. It can also be extended to more catgeories/classes as well.\n",
+    "The following quantities are then used\n",
+    "1. positive condition number $P$, which represents the number of real positive cases in the data (output one/true etc)\n",
+    "\n",
+    "2. The condition negative number $N$ which is the number of negative cases (ouput zero/false etc)\n",
+    "\n",
+    "3. The true positive number $TP$ which represents whether a positive test result has been correctly classified (the application of our trained model on a test data set)\n",
+    "\n",
+    "4. The true negative $TN$ number which represents whether a negative test has been correctly classified\n",
+    "\n",
+    "5. The false positive $FP$ number, a so-called type I error which tells us about the fraction of  positive test result which are wrongly classified\n",
+    "\n",
+    "6. A false negative $FN$ number, a so-called type II error which, should be pretty obvious, indicates if a negative test has been wrongly classified.\n",
+    "\n",
+    "It is is easy to think in terms of illness. You could think of the above as\n",
+    "1. True positive: Sick people correctly identified as sick\n",
+    "\n",
+    "2. False positive: Healthy people incorrectly identified as sick\n",
+    "\n",
+    "3. True negative: Healthy people correctly identified as healthy\n",
+    "\n",
+    "4. False negative: Sick people incorrectly identified as healthy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfbe0571",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Combinations of classification results\n",
+    "\n",
+    "It is common in the literature to define various combinations the above numbers. The most commonly used are\n",
+    "\n",
+    "**Sensitivity, recall, hit rate, or true positive rate $TPR$. It is the probability of a positive test result, conditioned on the individual truly being positive.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f7c63d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {TPR} ={\\frac {\\mathrm {TP} }{\\mathrm {P} }}={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FN} }}=1-\\mathrm {FNR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e674d10",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The $TPR$ defines how many correct positive results occur among all positive samples available during the test\n",
+    "\n",
+    "**Miss rate or false negative rate $FNR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65577837",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FNR} ={\\frac {\\mathrm {FN} }{\\mathrm {P} }}={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TP} }} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5e8802d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**Specificity, selectivity or true negative rate $TNR$. It is the probability of a negative test result, conditioned on the individual truly being negative.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07c61591",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {TNR} ={\\frac {\\mathrm {TN} }{\\mathrm {N} }}={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FP} }}=1-\\mathrm {FPR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3f61bb3",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "with the fall-out false positive rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6544105e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FPR} ={\\frac {\\mathrm {FP} }{\\mathrm {N} }}={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TN} }}=1-\\mathrm {TNR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b571e85",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The $FPR$ defines how many incorrect positive results occur among\n",
+    "all negative samples available during the test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17d6872a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Positive and negative prediction values\n",
+    "\n",
+    "The positive and negative predictive values \n",
+    "are the proportions of positive and negative results in statistics and\n",
+    "diagnostic tests that are true positive and true negative results,\n",
+    "respectively.[1] The PPV and NPV describe the performance of a\n",
+    "diagnostic test or other statistical measure. A high result can be\n",
+    "interpreted as indicating the accuracy of such a statistic.\n",
+    "\n",
+    "**Precision or positive predictive value $PPV$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "122f7e6c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {PPV} ={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FP} }}=1-\\mathrm {FDR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f44a5e78",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**Negative predictive value $NPV$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86fa9670",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {NPV} ={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FN} }}=1-\\mathrm {FOR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a30e358d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Other quantities\n",
+    "\n",
+    "**False discovery rate $FDR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f0de6de",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FDR} ={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TP} }}=1-\\mathrm {PPV} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4cc93f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**False omission rate $FOR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4866ec0a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FOR} ={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TN} }}=1-\\mathrm {NPV} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1c8c45",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## $F_1$ score\n",
+    "\n",
+    "In statistical analysis of binary classification, the F-score or\n",
+    "F-measure is a measure of a test's accuracy. It is calculated from the\n",
+    "precision and recall of the test, where the precision is the number of\n",
+    "true positive results divided by the number of all positive results,\n",
+    "including those not identified correctly, and the recall is the number\n",
+    "of true positive results divided by the number of all samples that\n",
+    "should have been identified as positive. Precision is also known as\n",
+    "positive predictive value, and recall is also known as sensitivity in\n",
+    "diagnostic binary classification.\n",
+    "\n",
+    "The F1 score is the harmonic mean of the precision and recall. It thus\n",
+    "symmetrically represents both precision and recall in one metric.  The\n",
+    "highest possible value of an F-score is 1.0, indicating perfect\n",
+    "precision and recall, and the lowest possible value is 0, if either\n",
+    "precision or recall are zero.\n",
+    "\n",
+    "It is defined as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e651a1f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {F} _{1}=2\\times {\\frac {\\mathrm {PPV} \\times \\mathrm {TPR} }{\\mathrm {PPV} +\\mathrm {TPR} }}={\\frac {2\\mathrm {TP} }{2\\mathrm {TP} +\\mathrm {FP} +\\mathrm {FN} }}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d83efe41",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## ROC curve\n",
+    "\n",
+    "A receiver operating characteristic curve, or ROC curve, is a\n",
+    "graphical plot that illustrates the performance of a binary classifier\n",
+    "model at varying threshold values.\n",
+    "\n",
+    "The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.\n",
+    "\n",
+    "To draw a ROC curve, only the true positive rate (TPR) and false\n",
+    "positive rate (FPR) are needed (as functions of some classifier\n",
+    "parameter). The TPR defines how many correct positive results occur\n",
+    "among all positive samples available during the test. FPR, on the\n",
+    "other hand, defines how many incorrect positive results occur among\n",
+    "all negative samples available during the test.\n",
+    "\n",
+    "See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d088215b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Cumulative gain curve\n",
+    "\n",
+    "The cumulative gain curve is a performance evaluation used typically for binary classification problems.\n",
+    "It plots the $TPR$ True Positive Rate or Sensitivity (which represents the \n",
+    "fraction of examples correctly classified\n",
+    "against Predictive Positive Rate, which represents \n",
+    "the fraction of positively predicted examples.\n",
+    "\n",
+    "The examples below show the confusion matrix, the ROC curve and the cumulative gain for the Wisconsin cancer data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c0bcd1f",
    "metadata": {
     "editable": true
    },
@@ -601,7 +921,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "00c15782",
+   "id": "2a6f80df",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -612,7 +932,10 @@
      "output_type": "stream",
      "text": [
       "(426, 30)\n",
-      "(143, 30)\n"
+      "(143, 30)\n",
+      "[1.         0.86666667 1.         0.92857143 1.         0.85714286\n",
+      " 1.         0.92857143 0.92857143 1.        ]\n",
+      "Test set accuracy with Logistic Regression: 0.94\n"
      ]
     },
     {
@@ -709,15 +1032,6 @@
       "  n_iter_i = _check_optimize_result(\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.         0.86666667 1.         0.92857143 1.         0.85714286\n",
-      " 1.         0.92857143 0.92857143 1.        ]\n",
-      "Test set accuracy with Logistic Regression: 0.94\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -727,7 +1041,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_22_3.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_44_2.png"
       }
      },
      "output_type": "display_data"
@@ -741,7 +1055,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_22_4.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_44_3.png"
       }
      },
      "output_type": "display_data"
@@ -755,7 +1069,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_22_5.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_44_4.png"
       }
      },
      "output_type": "display_data"
@@ -798,40 +1112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7a8eeb7",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## ROC curve\n",
-    "\n",
-    "A receiver operating characteristic curve, or ROC curve, is a\n",
-    "graphical plot that illustrates the performance of a binary classifier\n",
-    "model at varying threshold values.\n",
-    "\n",
-    "The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.\n",
-    "See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6414e652",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Cumulative gain curve\n",
-    "\n",
-    "The cumulative gain curve is a performance evaluation used typically for binary classification problems.\n",
-    "It plots the $TPR$ True Positive Rate or Sensitivity (which represents the \n",
-    "fraction of examples correctly classified\n",
-    "against Predictive Positive Rate, which represents \n",
-    "the fraction of positively predicted examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7646c025",
+   "id": "58e5b521",
    "metadata": {
     "editable": true
    },
@@ -841,7 +1122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7b4b23b",
+   "id": "4ebe5a91",
    "metadata": {
     "editable": true
    },
@@ -868,7 +1149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "741bf972",
+   "id": "182f425e",
    "metadata": {
     "editable": true
    },
@@ -879,7 +1160,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f9060416",
+   "id": "8b3ca785",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1008,14 +1289,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-07 06:16:59.648382: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "2023-11-08 06:51:57.259901: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 3s - loss: 0.9885 - 3s/epoch - 66ms/step\n"
+      "50/50 - 3s - loss: 2.5680 - 3s/epoch - 69ms/step\n"
      ]
     },
     {
@@ -1029,7 +1310,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4316 - 465ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 1.8934 - 563ms/epoch - 11ms/step\n"
      ]
     },
     {
@@ -1043,7 +1324,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4150 - 494ms/epoch - 10ms/step\n"
+      "50/50 - 1s - loss: 1.4588 - 512ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1057,7 +1338,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4066 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.8688 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1071,7 +1352,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4037 - 497ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.4871 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1085,7 +1366,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.4013 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4162 - 456ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1099,7 +1380,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3980 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4068 - 451ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1113,7 +1394,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3981 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4032 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1127,7 +1408,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3944 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4009 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1141,7 +1422,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3951 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3978 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1155,7 +1436,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3929 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3964 - 451ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1169,7 +1450,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3929 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3936 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1183,7 +1464,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3915 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3933 - 450ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1197,7 +1478,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3923 - 472ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3938 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1211,7 +1492,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3901 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3924 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1225,7 +1506,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3893 - 471ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3924 - 456ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1239,7 +1520,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3893 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3919 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1253,7 +1534,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3893 - 490ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3907 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1267,7 +1548,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3888 - 475ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3906 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1281,7 +1562,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3866 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3907 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1295,7 +1576,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3875 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3888 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1309,7 +1590,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3864 - 472ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3898 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1323,7 +1604,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3883 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3889 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1337,7 +1618,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3877 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3883 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1351,7 +1632,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3884 - 491ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3885 - 456ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1365,7 +1646,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3862 - 472ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3886 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1379,7 +1660,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3844 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3885 - 457ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1393,7 +1674,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3869 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3872 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1407,7 +1688,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3859 - 470ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3885 - 452ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1421,7 +1702,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3849 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1435,7 +1716,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3861 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3869 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1449,7 +1730,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3837 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3868 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1463,7 +1744,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3844 - 477ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3869 - 453ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1477,7 +1758,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3842 - 479ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3872 - 455ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1491,7 +1772,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3825 - 476ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3856 - 481ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1505,7 +1786,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3849 - 477ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3853 - 454ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1519,7 +1800,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3826 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3859 - 483ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1533,7 +1814,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3816 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3862 - 482ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1547,7 +1828,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3843 - 1s/epoch - 25ms/step\n"
+      "50/50 - 1s - loss: 0.3850 - 508ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1561,7 +1842,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3821 - 522ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3841 - 494ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1575,7 +1856,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3825 - 486ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3848 - 473ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1589,7 +1870,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3822 - 480ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3850 - 479ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1603,7 +1884,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3836 - 521ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3841 - 473ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1617,7 +1898,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3827 - 566ms/epoch - 11ms/step\n"
+      "50/50 - 0s - loss: 0.3847 - 479ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1631,7 +1912,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3815 - 504ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3810 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1645,7 +1926,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 1s - loss: 0.3832 - 511ms/epoch - 10ms/step\n"
+      "50/50 - 0s - loss: 0.3843 - 483ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1655,6 +1936,48 @@
       "Epoch 47/100\n"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50/50 - 0s - loss: 0.3830 - 467ms/epoch - 9ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 48/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50/50 - 0s - loss: 0.3827 - 485ms/epoch - 10ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 49/100\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50/50 - 0s - loss: 0.3820 - 479ms/epoch - 10ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 50/100\n"
+     ]
+    },
     {
      "ename": "KeyboardInterrupt",
      "evalue": "",
@@ -1743,7 +2066,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0342b0b",
+   "id": "b0c12b4c",
    "metadata": {
     "editable": true
    },
@@ -1763,7 +2086,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58fdc710",
+   "id": "d81020f6",
    "metadata": {
     "editable": true
    },
@@ -1779,7 +2102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f793c129",
+   "id": "2d7453c9",
    "metadata": {
     "editable": true
    },
@@ -1801,7 +2124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7446349",
+   "id": "fc4a082e",
    "metadata": {
     "editable": true
    },
@@ -1819,7 +2142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fac85e06",
+   "id": "c1106ad9",
    "metadata": {
     "editable": true
    },
@@ -1865,7 +2188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e3fbdb7",
+   "id": "b139ef7b",
    "metadata": {
     "editable": true
    },
@@ -1886,7 +2209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f29008fb",
+   "id": "70c95078",
    "metadata": {
     "editable": true
    },
@@ -1947,7 +2270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dedfb5dd",
+   "id": "2ca24031",
    "metadata": {
     "editable": true
    },
@@ -1972,7 +2295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2abfa20e",
+   "id": "93648979",
    "metadata": {
     "editable": true
    },
@@ -1991,7 +2314,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abc4dabb",
+   "id": "b8806dcd",
    "metadata": {
     "editable": true
    },
@@ -2015,7 +2338,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67e1ca86",
+   "id": "b0edad40",
    "metadata": {
     "editable": true
    },
@@ -2097,7 +2420,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc04ebe9",
+   "id": "58fd18f9",
    "metadata": {
     "editable": true
    },
@@ -2113,7 +2436,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "a5882cbb",
+   "id": "a604caa5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2152,7 +2475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18221ec4",
+   "id": "545336c9",
    "metadata": {
     "editable": true
    },
@@ -2196,7 +2519,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "d4982f3a",
+   "id": "3ef3ed13",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2279,7 +2602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a146c2c5",
+   "id": "8a8b29b8",
    "metadata": {
     "editable": true
    },
@@ -2290,7 +2613,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "2f7691d6",
+   "id": "d840beea",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2392,7 +2715,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "618a775c",
+   "id": "2e5b339f",
    "metadata": {
     "editable": true
    },
@@ -2414,7 +2737,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "5f955304",
+   "id": "370e799d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2511,7 +2834,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae8bb1a0",
+   "id": "d8f62fc4",
    "metadata": {
     "editable": true
    },
@@ -2537,7 +2860,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "701451d4",
+   "id": "4ea36ea9",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.py b/doc/LectureNotes/_build/jupyter_execute/week45.py
index 76685246f..71cf54391 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week45.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week45.py
@@ -334,6 +334,150 @@ correlation_matrix = cancerpd.corr().round(1)
 # the classical Principal Component Analysis (PCA) theorem with
 # applications. This will be discussed later this semester ([week 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-bs.html)).
 
+# ## Other ways of presenting a classification problem
+# 
+# For a binary classifcation matrix, the so-called **confusion matrix**, is often used. It can also be extended to more catgeories/classes as well.
+# The following quantities are then used
+# 1. positive condition number $P$, which represents the number of real positive cases in the data (output one/true etc)
+# 
+# 2. The condition negative number $N$ which is the number of negative cases (ouput zero/false etc)
+# 
+# 3. The true positive number $TP$ which represents whether a positive test result has been correctly classified (the application of our trained model on a test data set)
+# 
+# 4. The true negative $TN$ number which represents whether a negative test has been correctly classified
+# 
+# 5. The false positive $FP$ number, a so-called type I error which tells us about the fraction of  positive test result which are wrongly classified
+# 
+# 6. A false negative $FN$ number, a so-called type II error which, should be pretty obvious, indicates if a negative test has been wrongly classified.
+# 
+# It is is easy to think in terms of illness. You could think of the above as
+# 1. True positive: Sick people correctly identified as sick
+# 
+# 2. False positive: Healthy people incorrectly identified as sick
+# 
+# 3. True negative: Healthy people correctly identified as healthy
+# 
+# 4. False negative: Sick people incorrectly identified as healthy
+
+# ## Combinations of classification results
+# 
+# It is common in the literature to define various combinations the above numbers. The most commonly used are
+# 
+# **Sensitivity, recall, hit rate, or true positive rate $TPR$. It is the probability of a positive test result, conditioned on the individual truly being positive.**
+
+# $$
+# {\displaystyle \mathrm {TPR} ={\frac {\mathrm {TP} }{\mathrm {P} }}={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FN} }}=1-\mathrm {FNR} }
+# $$
+
+# The $TPR$ defines how many correct positive results occur among all positive samples available during the test
+# 
+# **Miss rate or false negative rate $FNR$.**
+
+# $$
+# {\displaystyle \mathrm {FNR} ={\frac {\mathrm {FN} }{\mathrm {P} }}={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TP} }} }
+# $$
+
+# **Specificity, selectivity or true negative rate $TNR$. It is the probability of a negative test result, conditioned on the individual truly being negative.**
+
+# $$
+# {\displaystyle \mathrm {TNR} ={\frac {\mathrm {TN} }{\mathrm {N} }}={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FP} }}=1-\mathrm {FPR} }
+# $$
+
+# with the fall-out false positive rate
+
+# $$
+# {\displaystyle \mathrm {FPR} ={\frac {\mathrm {FP} }{\mathrm {N} }}={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TN} }}=1-\mathrm {TNR} }
+# $$
+
+# The $FPR$ defines how many incorrect positive results occur among
+# all negative samples available during the test.
+
+# ## Positive and negative prediction values
+# 
+# The positive and negative predictive values 
+# are the proportions of positive and negative results in statistics and
+# diagnostic tests that are true positive and true negative results,
+# respectively.[1] The PPV and NPV describe the performance of a
+# diagnostic test or other statistical measure. A high result can be
+# interpreted as indicating the accuracy of such a statistic.
+# 
+# **Precision or positive predictive value $PPV$.**
+
+# $$
+# {\displaystyle \mathrm {PPV} ={\frac {\mathrm {TP} }{\mathrm {TP} +\mathrm {FP} }}=1-\mathrm {FDR} }
+# $$
+
+# **Negative predictive value $NPV$.**
+
+# $$
+# {\displaystyle \mathrm {NPV} ={\frac {\mathrm {TN} }{\mathrm {TN} +\mathrm {FN} }}=1-\mathrm {FOR} }
+# $$
+
+# ## Other quantities
+# 
+# **False discovery rate $FDR$.**
+
+# $$
+# {\displaystyle \mathrm {FDR} ={\frac {\mathrm {FP} }{\mathrm {FP} +\mathrm {TP} }}=1-\mathrm {PPV} }
+# $$
+
+# **False omission rate $FOR$.**
+
+# $$
+# {\displaystyle \mathrm {FOR} ={\frac {\mathrm {FN} }{\mathrm {FN} +\mathrm {TN} }}=1-\mathrm {NPV} }
+# $$
+
+# ## $F_1$ score
+# 
+# In statistical analysis of binary classification, the F-score or
+# F-measure is a measure of a test's accuracy. It is calculated from the
+# precision and recall of the test, where the precision is the number of
+# true positive results divided by the number of all positive results,
+# including those not identified correctly, and the recall is the number
+# of true positive results divided by the number of all samples that
+# should have been identified as positive. Precision is also known as
+# positive predictive value, and recall is also known as sensitivity in
+# diagnostic binary classification.
+# 
+# The F1 score is the harmonic mean of the precision and recall. It thus
+# symmetrically represents both precision and recall in one metric.  The
+# highest possible value of an F-score is 1.0, indicating perfect
+# precision and recall, and the lowest possible value is 0, if either
+# precision or recall are zero.
+# 
+# It is defined as
+
+# $$
+# {\displaystyle \mathrm {F} _{1}=2\times {\frac {\mathrm {PPV} \times \mathrm {TPR} }{\mathrm {PPV} +\mathrm {TPR} }}={\frac {2\mathrm {TP} }{2\mathrm {TP} +\mathrm {FP} +\mathrm {FN} }}}
+# $$
+
+# ## ROC curve
+# 
+# A receiver operating characteristic curve, or ROC curve, is a
+# graphical plot that illustrates the performance of a binary classifier
+# model at varying threshold values.
+# 
+# The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.
+# 
+# To draw a ROC curve, only the true positive rate (TPR) and false
+# positive rate (FPR) are needed (as functions of some classifier
+# parameter). The TPR defines how many correct positive results occur
+# among all positive samples available during the test. FPR, on the
+# other hand, defines how many incorrect positive results occur among
+# all negative samples available during the test.
+# 
+# See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions.
+
+# ## Cumulative gain curve
+# 
+# The cumulative gain curve is a performance evaluation used typically for binary classification problems.
+# It plots the $TPR$ True Positive Rate or Sensitivity (which represents the 
+# fraction of examples correctly classified
+# against Predictive Positive Rate, which represents 
+# the fraction of positively predicted examples.
+# 
+# The examples below show the confusion matrix, the ROC curve and the cumulative gain for the Wisconsin cancer data.
+
 # ## Other measures in classification studies: Cancer Data  again
 
 # In[8]:
@@ -373,23 +517,6 @@ skplt.metrics.plot_cumulative_gain(y_test, y_probas)
 plt.show()
 
 
-# ## ROC curve
-# 
-# A receiver operating characteristic curve, or ROC curve, is a
-# graphical plot that illustrates the performance of a binary classifier
-# model at varying threshold values.
-# 
-# The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.
-# See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions.
-
-# ## Cumulative gain curve
-# 
-# The cumulative gain curve is a performance evaluation used typically for binary classification problems.
-# It plots the $TPR$ True Positive Rate or Sensitivity (which represents the 
-# fraction of examples correctly classified
-# against Predictive Positive Rate, which represents 
-# the fraction of positively predicted examples.
-
 # ## Material for Lecture Thursday November 9
 
 # ## Recurrent neural networks (RNNs): Overarching view
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45_44_2.png b/doc/LectureNotes/_build/jupyter_execute/week45_44_2.png
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diff --git a/doc/LectureNotes/week45.ipynb b/doc/LectureNotes/week45.ipynb
index 5ce32f797..749491e98 100644
--- a/doc/LectureNotes/week45.ipynb
+++ b/doc/LectureNotes/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ddffe3be",
+   "id": "31a10e2b",
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@@ -14,7 +14,7 @@
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   {
    "cell_type": "markdown",
-   "id": "cde0bbff",
+   "id": "019daa83",
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@@ -27,7 +27,7 @@
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    "cell_type": "markdown",
-   "id": "f48c6388",
+   "id": "6ecf1c2b",
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@@ -67,7 +67,7 @@
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   {
    "cell_type": "markdown",
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+   "id": "d80a4db8",
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@@ -77,7 +77,7 @@
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-   "id": "4d810bbf",
+   "id": "1ded5c92",
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@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "39f540b5",
+   "id": "6dea0513",
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     "editable": true
@@ -153,7 +153,7 @@
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   {
    "cell_type": "markdown",
-   "id": "09d24c34",
+   "id": "440bf579",
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@@ -165,7 +165,7 @@
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    "cell_type": "markdown",
-   "id": "7c84429f",
+   "id": "683b1da2",
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@@ -180,7 +180,7 @@
   {
    "cell_type": "code",
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-   "id": "ac8fda39",
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@@ -233,7 +233,7 @@
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@@ -248,7 +248,7 @@
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    "cell_type": "markdown",
-   "id": "afe2e276",
+   "id": "a9798c07",
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@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
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-   "id": "5707b319",
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     "editable": true
@@ -322,7 +322,7 @@
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   {
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-   "id": "8f067ea7",
+   "id": "dcd07cb8",
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@@ -337,7 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "cfe2ed78",
+   "id": "65061d95",
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     "editable": true
@@ -364,7 +364,7 @@
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   {
    "cell_type": "markdown",
-   "id": "97a315dc",
+   "id": "50492453",
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@@ -378,7 +378,7 @@
   {
    "cell_type": "code",
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-   "id": "9363662a",
+   "id": "08a2ab17",
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     "editable": true
@@ -423,7 +423,7 @@
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   {
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+   "id": "5408b7dd",
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@@ -448,7 +448,7 @@
   {
    "cell_type": "code",
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-   "id": "9db77f36",
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@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
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+   "id": "83903231",
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@@ -471,7 +471,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "926cdfd7",
+   "id": "58ccd6c3",
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     "editable": true
@@ -483,7 +483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bd13984",
+   "id": "adda54cb",
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     "editable": true
    },
@@ -496,7 +496,327 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55242d6d",
+   "id": "8799c034",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Other ways of presenting a classification problem\n",
+    "\n",
+    "For a binary classifcation matrix, the so-called **confusion matrix**, is often used. It can also be extended to more catgeories/classes as well.\n",
+    "The following quantities are then used\n",
+    "1. positive condition number $P$, which represents the number of real positive cases in the data (output one/true etc)\n",
+    "\n",
+    "2. The condition negative number $N$ which is the number of negative cases (ouput zero/false etc)\n",
+    "\n",
+    "3. The true positive number $TP$ which represents whether a positive test result has been correctly classified (the application of our trained model on a test data set)\n",
+    "\n",
+    "4. The true negative $TN$ number which represents whether a negative test has been correctly classified\n",
+    "\n",
+    "5. The false positive $FP$ number, a so-called type I error which tells us about the fraction of  positive test result which are wrongly classified\n",
+    "\n",
+    "6. A false negative $FN$ number, a so-called type II error which, should be pretty obvious, indicates if a negative test has been wrongly classified.\n",
+    "\n",
+    "It is is easy to think in terms of illness. You could think of the above as\n",
+    "1. True positive: Sick people correctly identified as sick\n",
+    "\n",
+    "2. False positive: Healthy people incorrectly identified as sick\n",
+    "\n",
+    "3. True negative: Healthy people correctly identified as healthy\n",
+    "\n",
+    "4. False negative: Sick people incorrectly identified as healthy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfbe0571",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Combinations of classification results\n",
+    "\n",
+    "It is common in the literature to define various combinations the above numbers. The most commonly used are\n",
+    "\n",
+    "**Sensitivity, recall, hit rate, or true positive rate $TPR$. It is the probability of a positive test result, conditioned on the individual truly being positive.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f7c63d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {TPR} ={\\frac {\\mathrm {TP} }{\\mathrm {P} }}={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FN} }}=1-\\mathrm {FNR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e674d10",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The $TPR$ defines how many correct positive results occur among all positive samples available during the test\n",
+    "\n",
+    "**Miss rate or false negative rate $FNR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65577837",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FNR} ={\\frac {\\mathrm {FN} }{\\mathrm {P} }}={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TP} }} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5e8802d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**Specificity, selectivity or true negative rate $TNR$. It is the probability of a negative test result, conditioned on the individual truly being negative.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07c61591",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {TNR} ={\\frac {\\mathrm {TN} }{\\mathrm {N} }}={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FP} }}=1-\\mathrm {FPR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3f61bb3",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "with the fall-out false positive rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6544105e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FPR} ={\\frac {\\mathrm {FP} }{\\mathrm {N} }}={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TN} }}=1-\\mathrm {TNR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b571e85",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The $FPR$ defines how many incorrect positive results occur among\n",
+    "all negative samples available during the test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17d6872a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Positive and negative prediction values\n",
+    "\n",
+    "The positive and negative predictive values \n",
+    "are the proportions of positive and negative results in statistics and\n",
+    "diagnostic tests that are true positive and true negative results,\n",
+    "respectively.[1] The PPV and NPV describe the performance of a\n",
+    "diagnostic test or other statistical measure. A high result can be\n",
+    "interpreted as indicating the accuracy of such a statistic.\n",
+    "\n",
+    "**Precision or positive predictive value $PPV$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "122f7e6c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {PPV} ={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FP} }}=1-\\mathrm {FDR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f44a5e78",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**Negative predictive value $NPV$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86fa9670",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {NPV} ={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FN} }}=1-\\mathrm {FOR} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a30e358d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Other quantities\n",
+    "\n",
+    "**False discovery rate $FDR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f0de6de",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FDR} ={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TP} }}=1-\\mathrm {PPV} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4cc93f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**False omission rate $FOR$.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4866ec0a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {FOR} ={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TN} }}=1-\\mathrm {NPV} }\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1c8c45",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## $F_1$ score\n",
+    "\n",
+    "In statistical analysis of binary classification, the F-score or\n",
+    "F-measure is a measure of a test's accuracy. It is calculated from the\n",
+    "precision and recall of the test, where the precision is the number of\n",
+    "true positive results divided by the number of all positive results,\n",
+    "including those not identified correctly, and the recall is the number\n",
+    "of true positive results divided by the number of all samples that\n",
+    "should have been identified as positive. Precision is also known as\n",
+    "positive predictive value, and recall is also known as sensitivity in\n",
+    "diagnostic binary classification.\n",
+    "\n",
+    "The F1 score is the harmonic mean of the precision and recall. It thus\n",
+    "symmetrically represents both precision and recall in one metric.  The\n",
+    "highest possible value of an F-score is 1.0, indicating perfect\n",
+    "precision and recall, and the lowest possible value is 0, if either\n",
+    "precision or recall are zero.\n",
+    "\n",
+    "It is defined as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e651a1f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "{\\displaystyle \\mathrm {F} _{1}=2\\times {\\frac {\\mathrm {PPV} \\times \\mathrm {TPR} }{\\mathrm {PPV} +\\mathrm {TPR} }}={\\frac {2\\mathrm {TP} }{2\\mathrm {TP} +\\mathrm {FP} +\\mathrm {FN} }}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d83efe41",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## ROC curve\n",
+    "\n",
+    "A receiver operating characteristic curve, or ROC curve, is a\n",
+    "graphical plot that illustrates the performance of a binary classifier\n",
+    "model at varying threshold values.\n",
+    "\n",
+    "The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.\n",
+    "\n",
+    "To draw a ROC curve, only the true positive rate (TPR) and false\n",
+    "positive rate (FPR) are needed (as functions of some classifier\n",
+    "parameter). The TPR defines how many correct positive results occur\n",
+    "among all positive samples available during the test. FPR, on the\n",
+    "other hand, defines how many incorrect positive results occur among\n",
+    "all negative samples available during the test.\n",
+    "\n",
+    "See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d088215b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Cumulative gain curve\n",
+    "\n",
+    "The cumulative gain curve is a performance evaluation used typically for binary classification problems.\n",
+    "It plots the $TPR$ True Positive Rate or Sensitivity (which represents the \n",
+    "fraction of examples correctly classified\n",
+    "against Predictive Positive Rate, which represents \n",
+    "the fraction of positively predicted examples.\n",
+    "\n",
+    "The examples below show the confusion matrix, the ROC curve and the cumulative gain for the Wisconsin cancer data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c0bcd1f",
    "metadata": {
     "editable": true
    },
@@ -507,7 +827,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "00c15782",
+   "id": "2a6f80df",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -550,40 +870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7a8eeb7",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## ROC curve\n",
-    "\n",
-    "A receiver operating characteristic curve, or ROC curve, is a\n",
-    "graphical plot that illustrates the performance of a binary classifier\n",
-    "model at varying threshold values.\n",
-    "\n",
-    "The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.\n",
-    "See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6414e652",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Cumulative gain curve\n",
-    "\n",
-    "The cumulative gain curve is a performance evaluation used typically for binary classification problems.\n",
-    "It plots the $TPR$ True Positive Rate or Sensitivity (which represents the \n",
-    "fraction of examples correctly classified\n",
-    "against Predictive Positive Rate, which represents \n",
-    "the fraction of positively predicted examples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7646c025",
+   "id": "58e5b521",
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@@ -593,7 +880,7 @@
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@@ -740,7 +1027,7 @@
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