From 2e2f61f8de0aacf083d730b0b51de556ba09b1e6 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Tue, 7 Nov 2023 06:18:24 +0100
Subject: [PATCH] update notes

---
 .../_build/.doctrees/environment.pickle       | Bin 588174 -> 589101 bytes
 .../_build/.doctrees/week45.doctree           | Bin 907494 -> 910077 bytes
 .../_build/html/_images/week45_22_3.png       | Bin 45043 -> 17131 bytes
 .../_build/html/_images/week45_22_4.png       | Bin 39374 -> 45043 bytes
 .../_build/html/_images/week45_22_5.png       | Bin 0 -> 39374 bytes
 .../_build/html/_sources/week45.ipynb         | 129 ++++---
 doc/LectureNotes/_build/html/searchindex.js   |   2 +-
 doc/LectureNotes/_build/html/week45.html      | 184 +++++-----
 .../_build/jupyter_execute/week45.ipynb       | 343 ++++++++----------
 .../_build/jupyter_execute/week45.py          |  17 +
 .../_build/jupyter_execute/week45_22_3.png    | Bin 45043 -> 17131 bytes
 .../_build/jupyter_execute/week45_22_4.png    | Bin 39374 -> 45043 bytes
 .../_build/jupyter_execute/week45_22_5.png    | Bin 0 -> 39374 bytes
 doc/LectureNotes/week45.ipynb                 | 129 ++++---
 14 files changed, 412 insertions(+), 392 deletions(-)
 create mode 100644 doc/LectureNotes/_build/html/_images/week45_22_5.png
 create mode 100644 doc/LectureNotes/_build/jupyter_execute/week45_22_5.png

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 8f8681fa76d963c6cfefaf0ab5b4b116498f5a06..4e511105ab1d07841e7be066f3bf54799669801b 100644
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diff --git a/doc/LectureNotes/_build/html/_sources/week45.ipynb b/doc/LectureNotes/_build/html/_sources/week45.ipynb
index 6a8c0abea..5ce32f797 100644
--- a/doc/LectureNotes/_build/html/_sources/week45.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week45.ipynb
@@ -2,7 +2,7 @@
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@@ -14,7 +14,7 @@
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@@ -27,7 +27,7 @@
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@@ -67,7 +67,7 @@
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@@ -96,7 +96,7 @@
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@@ -337,7 +337,7 @@
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@@ -364,7 +364,7 @@
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@@ -378,7 +378,7 @@
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@@ -423,7 +423,7 @@
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@@ -483,7 +483,7 @@
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   {
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+   },
+   "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."
+   ]
+  },
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@@ -1041,7 +1074,7 @@
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@@ -1218,7 +1251,7 @@
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@@ -1342,7 +1375,7 @@
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diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index d2d646177..8014fab54 100644
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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,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:42,funtion:[40,41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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: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],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],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],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],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],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 fe1a7fe99..6785af08a 100644
--- a/doc/LectureNotes/_build/html/week45.html
+++ b/doc/LectureNotes/_build/html/week45.html
@@ -489,6 +489,16 @@ const thebe_selector_output = ".output, .cell_output"
    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="#roc-curve">
+   ROC curve
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#cumulative-gain-curve">
+   Cumulative gain curve
+  </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
@@ -658,6 +668,16 @@ const thebe_selector_output = ".output, .cell_output"
    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="#roc-curve">
+   ROC curve
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#cumulative-gain-curve">
+   Cumulative gain curve
+  </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
@@ -999,7 +1019,7 @@ 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 0x168f3fca0&gt;})
+                   param_distributions={&#39;alpha&#39;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x105b84cd0&gt;})
 Best estimated lambda-value: 0.9849967686928113
 MSE score: 1.0853136633465326
 R2 score: -0.0002382102844775691
@@ -1177,9 +1197,6 @@ 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):
@@ -1272,12 +1289,33 @@ Please also refer to the documentation for alternative solver options:
   n_iter_i = _check_optimize_result(
 </pre></div>
 </div>
-<img alt="_images/week45_22_2.png" src="_images/week45_22_2.png" />
+<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" />
 </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>
@@ -1419,327 +1457,285 @@ 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>2023-11-06 06:26:15.664586: 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>2023-11-07 06:16:59.648382: 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: 0.4727 - 3s/epoch - 66ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 3s - loss: 0.9885 - 3s/epoch - 66ms/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: 0.3966 - 471ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4316 - 465ms/epoch - 9ms/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: 0.3929 - 470ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4150 - 494ms/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: 0.3901 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4066 - 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: 0.3920 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4037 - 497ms/epoch - 10ms/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: 0.3910 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4013 - 469ms/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: 0.3911 - 470ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3980 - 467ms/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: 0.3896 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3981 - 468ms/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: 0.3913 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3944 - 467ms/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: 0.3858 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3951 - 468ms/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: 0.3884 - 465ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3929 - 467ms/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: 0.3899 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3929 - 467ms/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: 0.3868 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3915 - 469ms/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: 0.3853 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3923 - 472ms/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: 0.3889 - 465ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3901 - 467ms/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: 0.3858 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 471ms/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: 0.3873 - 465ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 468ms/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: 0.3879 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 490ms/epoch - 10ms/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: 0.3870 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3888 - 475ms/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: 0.3842 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3866 - 468ms/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: 0.3847 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3875 - 469ms/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: 0.3846 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3864 - 472ms/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: 0.3837 - 470ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3883 - 467ms/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: 0.3855 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3877 - 469ms/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: 0.3846 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3884 - 491ms/epoch - 10ms/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: 0.3842 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3862 - 472ms/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: 0.3835 - 463ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3844 - 466ms/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: 0.3839 - 461ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3869 - 468ms/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: 0.3827 - 462ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3859 - 470ms/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: 0.3827 - 463ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3849 - 466ms/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: 0.3810 - 462ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3861 - 463ms/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: 0.3812 - 457ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3837 - 462ms/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: 0.3814 - 460ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3844 - 477ms/epoch - 10ms/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: 0.3828 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3842 - 479ms/epoch - 10ms/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: 0.3816 - 461ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3825 - 476ms/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: 0.3822 - 463ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3849 - 477ms/epoch - 10ms/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: 0.3813 - 462ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3826 - 463ms/epoch - 9ms/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: 0.3803 - 465ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3816 - 466ms/epoch - 9ms/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 - 0s - loss: 0.3801 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3843 - 1s/epoch - 25ms/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 - 0s - loss: 0.3815 - 467ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3821 - 522ms/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: 0.3806 - 464ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3825 - 486ms/epoch - 10ms/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: 0.3801 - 464ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3822 - 480ms/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 - 0s - loss: 0.3791 - 469ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3836 - 521ms/epoch - 10ms/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 - 0s - loss: 0.3803 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3827 - 566ms/epoch - 11ms/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 - 0s - loss: 0.3779 - 468ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3815 - 504ms/epoch - 10ms/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 - 0s - loss: 0.3776 - 466ms/epoch - 9ms/step
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3832 - 511ms/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: 0.3778 - 472ms/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: 0.3790 - 465ms/epoch - 9ms/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: 0.3750 - 466ms/epoch - 9ms/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 stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3782 - 468ms/epoch - 9ms/step
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 51/100
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3773 - 469ms/epoch - 9ms/step
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 52/100
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3768 - 466ms/epoch - 9ms/step
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 53/100
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3760 - 467ms/epoch - 9ms/step
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 54/100
-</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/week45.ipynb b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb
index 9d6c61875..96057fc92 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": "38177bc8",
+   "id": "ddffe3be",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a816084f",
+   "id": "cde0bbff",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f62a8799",
+   "id": "f48c6388",
    "metadata": {
     "editable": true
    },
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a50d9d2",
+   "id": "ba6964ff",
    "metadata": {
     "editable": true
    },
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30ed4f8b",
+   "id": "4d810bbf",
    "metadata": {
     "editable": true
    },
@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b6060ea0",
+   "id": "39f540b5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02a16352",
+   "id": "09d24c34",
    "metadata": {
     "editable": true
    },
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d15635eb",
+   "id": "7c84429f",
    "metadata": {
     "editable": true
    },
@@ -195,7 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3587f6a6",
+   "id": "ac8fda39",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5c4df1d",
+   "id": "b9da31b6",
    "metadata": {
     "editable": true
    },
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "334af746",
+   "id": "afe2e276",
    "metadata": {
     "editable": true
    },
@@ -297,7 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "04ba2582",
+   "id": "5707b319",
    "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 0x168f3fca0>})\n",
+      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x105b84cd0>})\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": "e713b0d3",
+   "id": "8f067ea7",
    "metadata": {
     "editable": true
    },
@@ -378,7 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a0304d86",
+   "id": "cfe2ed78",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -429,7 +429,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a67cf20f",
+   "id": "97a315dc",
    "metadata": {
     "editable": true
    },
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "78a673cf",
+   "id": "9363662a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e533f0d8",
+   "id": "298800d9",
    "metadata": {
     "editable": true
    },
@@ -542,7 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "88a99e3b",
+   "id": "9db77f36",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -554,7 +554,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1095373",
+   "id": "2872089d",
    "metadata": {
     "editable": true
    },
@@ -565,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "580dfa45",
+   "id": "926cdfd7",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f67b639",
+   "id": "6bd13984",
    "metadata": {
     "editable": true
    },
@@ -590,7 +590,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a42f0ef1",
+   "id": "55242d6d",
    "metadata": {
     "editable": true
    },
@@ -601,7 +601,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "286c60f7",
+   "id": "00c15782",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -612,10 +612,7 @@
      "output_type": "stream",
      "text": [
       "(426, 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"
+      "(143, 30)\n"
      ]
     },
     {
@@ -712,6 +709,15 @@
       "  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",
@@ -721,7 +727,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_22_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week45_22_3.png"
       }
      },
      "output_type": "display_data"
@@ -735,7 +741,7 @@
      },
      "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_22_4.png"
       }
      },
      "output_type": "display_data"
@@ -749,7 +755,7 @@
      },
      "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_22_5.png"
       }
      },
      "output_type": "display_data"
@@ -792,7 +798,40 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f126f5b",
+   "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",
    "metadata": {
     "editable": true
    },
@@ -802,7 +841,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf47aea8",
+   "id": "f7b4b23b",
    "metadata": {
     "editable": true
    },
@@ -829,7 +868,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43911e80",
+   "id": "741bf972",
    "metadata": {
     "editable": true
    },
@@ -840,7 +879,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "93a5e85c",
+   "id": "f9060416",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -969,14 +1008,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-06 06:26:15.664586: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
+      "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"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 3s - loss: 0.4727 - 3s/epoch - 66ms/step\n"
+      "50/50 - 3s - loss: 0.9885 - 3s/epoch - 66ms/step\n"
      ]
     },
     {
@@ -990,7 +1029,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3966 - 471ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4316 - 465ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1004,7 +1043,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3929 - 470ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4150 - 494ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1018,7 +1057,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3901 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4066 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1032,7 +1071,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3920 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4037 - 497ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1046,7 +1085,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3910 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.4013 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1060,7 +1099,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3911 - 470ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3980 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1074,7 +1113,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3896 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3981 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1088,7 +1127,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3913 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3944 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1102,7 +1141,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3858 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3951 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1116,7 +1155,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3884 - 465ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3929 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1130,7 +1169,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3899 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3929 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1144,7 +1183,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3868 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3915 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1158,7 +1197,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3853 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3923 - 472ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1172,7 +1211,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3889 - 465ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3901 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1186,7 +1225,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3858 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3893 - 471ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1200,7 +1239,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3873 - 465ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3893 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1214,7 +1253,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3879 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3893 - 490ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1228,7 +1267,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3870 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3888 - 475ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1242,7 +1281,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3842 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3866 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1256,7 +1295,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3847 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3875 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1270,7 +1309,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3846 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3864 - 472ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1284,7 +1323,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3837 - 470ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3883 - 467ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1298,7 +1337,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3855 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3877 - 469ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1312,7 +1351,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3846 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3884 - 491ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1326,7 +1365,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3842 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3862 - 472ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1340,7 +1379,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3835 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3844 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1354,7 +1393,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3839 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3869 - 468ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1368,7 +1407,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3827 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3859 - 470ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1382,7 +1421,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3827 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3849 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1396,7 +1435,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3810 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3861 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1410,7 +1449,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3812 - 457ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3837 - 462ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1424,7 +1463,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3814 - 460ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3844 - 477ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1438,7 +1477,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3828 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3842 - 479ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1452,7 +1491,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3816 - 461ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3825 - 476ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1466,7 +1505,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3822 - 463ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3849 - 477ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1480,7 +1519,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3813 - 462ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3826 - 463ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1494,7 +1533,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3803 - 465ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3816 - 466ms/epoch - 9ms/step\n"
      ]
     },
     {
@@ -1508,7 +1547,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3801 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3843 - 1s/epoch - 25ms/step\n"
      ]
     },
     {
@@ -1522,7 +1561,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3815 - 467ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3821 - 522ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1536,7 +1575,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3806 - 464ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3825 - 486ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1550,7 +1589,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3801 - 464ms/epoch - 9ms/step\n"
+      "50/50 - 0s - loss: 0.3822 - 480ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1564,7 +1603,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3791 - 469ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3836 - 521ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1578,7 +1617,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3803 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3827 - 566ms/epoch - 11ms/step\n"
      ]
     },
     {
@@ -1592,7 +1631,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3779 - 468ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3815 - 504ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1606,7 +1645,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "50/50 - 0s - loss: 0.3776 - 466ms/epoch - 9ms/step\n"
+      "50/50 - 1s - loss: 0.3832 - 511ms/epoch - 10ms/step\n"
      ]
     },
     {
@@ -1616,104 +1655,6 @@
       "Epoch 47/100\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3778 - 472ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 48/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3790 - 465ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 49/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3750 - 466ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 50/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3782 - 468ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 51/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3773 - 469ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 52/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3768 - 466ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 53/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50/50 - 0s - loss: 0.3760 - 467ms/epoch - 9ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 54/100\n"
-     ]
-    },
     {
      "ename": "KeyboardInterrupt",
      "evalue": "",
@@ -1802,7 +1743,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15c0abdc",
+   "id": "e0342b0b",
    "metadata": {
     "editable": true
    },
@@ -1822,7 +1763,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2c9969b",
+   "id": "58fdc710",
    "metadata": {
     "editable": true
    },
@@ -1838,7 +1779,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc82b4e6",
+   "id": "f793c129",
    "metadata": {
     "editable": true
    },
@@ -1860,7 +1801,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1dfa318",
+   "id": "b7446349",
    "metadata": {
     "editable": true
    },
@@ -1878,7 +1819,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bce88bd1",
+   "id": "fac85e06",
    "metadata": {
     "editable": true
    },
@@ -1924,7 +1865,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "630c5765",
+   "id": "7e3fbdb7",
    "metadata": {
     "editable": true
    },
@@ -1945,7 +1886,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74a05e7a",
+   "id": "f29008fb",
    "metadata": {
     "editable": true
    },
@@ -2006,7 +1947,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba29a00f",
+   "id": "dedfb5dd",
    "metadata": {
     "editable": true
    },
@@ -2031,7 +1972,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fb99b23",
+   "id": "2abfa20e",
    "metadata": {
     "editable": true
    },
@@ -2050,7 +1991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "640d0ffd",
+   "id": "abc4dabb",
    "metadata": {
     "editable": true
    },
@@ -2074,7 +2015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0aa61a82",
+   "id": "67e1ca86",
    "metadata": {
     "editable": true
    },
@@ -2156,7 +2097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05d59f36",
+   "id": "bc04ebe9",
    "metadata": {
     "editable": true
    },
@@ -2172,7 +2113,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b1bf0cab",
+   "id": "a5882cbb",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2211,7 +2152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6095262",
+   "id": "18221ec4",
    "metadata": {
     "editable": true
    },
@@ -2255,7 +2196,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "9eebf739",
+   "id": "d4982f3a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2338,7 +2279,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1f61819",
+   "id": "a146c2c5",
    "metadata": {
     "editable": true
    },
@@ -2349,7 +2290,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "eed2466a",
+   "id": "2f7691d6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2451,7 +2392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71855b2c",
+   "id": "618a775c",
    "metadata": {
     "editable": true
    },
@@ -2473,7 +2414,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "d1650ea8",
+   "id": "5f955304",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2570,7 +2511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32c4c59c",
+   "id": "ae8bb1a0",
    "metadata": {
     "editable": true
    },
@@ -2596,7 +2537,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "bbb48a15",
+   "id": "701451d4",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.py b/doc/LectureNotes/_build/jupyter_execute/week45.py
index 04e399c19..76685246f 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week45.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week45.py
@@ -373,6 +373,23 @@ 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_22_3.png b/doc/LectureNotes/_build/jupyter_execute/week45_22_3.png
index 0f4f7b9edb201f5e31b0f2f1c445d569e42b4d82..3f8036ab61771ae5afdfd1e5f225a0c3d20b23ca 100644
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diff --git a/doc/LectureNotes/week45.ipynb b/doc/LectureNotes/week45.ipynb
index 6a8c0abea..5ce32f797 100644
--- a/doc/LectureNotes/week45.ipynb
+++ b/doc/LectureNotes/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
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    "cell_type": "markdown",
-   "id": "38177bc8",
+   "id": "ddffe3be",
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@@ -14,7 +14,7 @@
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    "cell_type": "markdown",
-   "id": "a816084f",
+   "id": "cde0bbff",
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@@ -27,7 +27,7 @@
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-   "id": "f62a8799",
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@@ -67,7 +67,7 @@
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-   "id": "2a50d9d2",
+   "id": "ba6964ff",
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@@ -77,7 +77,7 @@
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@@ -96,7 +96,7 @@
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-   "id": "b6060ea0",
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@@ -153,7 +153,7 @@
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@@ -165,7 +165,7 @@
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@@ -180,7 +180,7 @@
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@@ -233,7 +233,7 @@
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@@ -248,7 +248,7 @@
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   {
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@@ -268,7 +268,7 @@
   {
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-   "id": "04ba2582",
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@@ -322,7 +322,7 @@
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-   "id": "e713b0d3",
+   "id": "8f067ea7",
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@@ -337,7 +337,7 @@
   {
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    "execution_count": 4,
-   "id": "a0304d86",
+   "id": "cfe2ed78",
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@@ -364,7 +364,7 @@
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   {
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+   "id": "97a315dc",
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@@ -378,7 +378,7 @@
   {
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    "execution_count": 5,
-   "id": "78a673cf",
+   "id": "9363662a",
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@@ -423,7 +423,7 @@
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   {
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-   "id": "e533f0d8",
+   "id": "298800d9",
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@@ -448,7 +448,7 @@
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@@ -460,7 +460,7 @@
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   {
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@@ -471,7 +471,7 @@
   {
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    "execution_count": 7,
-   "id": "580dfa45",
+   "id": "926cdfd7",
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@@ -483,7 +483,7 @@
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   {
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-   "id": "1f67b639",
+   "id": "6bd13984",
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@@ -496,7 +496,7 @@
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   {
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-   "id": "a42f0ef1",
+   "id": "55242d6d",
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@@ -507,7 +507,7 @@
   {
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    "execution_count": 8,
-   "id": "286c60f7",
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@@ -550,7 +550,40 @@
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   {
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-   "id": "5f126f5b",
+   "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",
    "metadata": {
     "editable": true
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@@ -560,7 +593,7 @@
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-   "id": "bf47aea8",
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@@ -587,7 +620,7 @@
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+   "id": "741bf972",
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@@ -598,7 +631,7 @@
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    "execution_count": 9,
-   "id": "93a5e85c",
+   "id": "f9060416",
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@@ -671,7 +704,7 @@
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-   "id": "15c0abdc",
+   "id": "e0342b0b",
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@@ -691,7 +724,7 @@
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-   "id": "e2c9969b",
+   "id": "58fdc710",
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@@ -707,7 +740,7 @@
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+   "id": "f793c129",
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@@ -729,7 +762,7 @@
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-   "id": "c1dfa318",
+   "id": "b7446349",
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@@ -747,7 +780,7 @@
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   {
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-   "id": "bce88bd1",
+   "id": "fac85e06",
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@@ -793,7 +826,7 @@
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   {
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-   "id": "630c5765",
+   "id": "7e3fbdb7",
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@@ -814,7 +847,7 @@
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-   "id": "74a05e7a",
+   "id": "f29008fb",
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@@ -875,7 +908,7 @@
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   {
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-   "id": "ba29a00f",
+   "id": "dedfb5dd",
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@@ -900,7 +933,7 @@
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   {
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-   "id": "4fb99b23",
+   "id": "2abfa20e",
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@@ -919,7 +952,7 @@
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-   "id": "640d0ffd",
+   "id": "abc4dabb",
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@@ -943,7 +976,7 @@
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-   "id": "0aa61a82",
+   "id": "67e1ca86",
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@@ -1025,7 +1058,7 @@
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-   "id": "05d59f36",
+   "id": "bc04ebe9",
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@@ -1041,7 +1074,7 @@
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    "execution_count": 10,
-   "id": "b1bf0cab",
+   "id": "a5882cbb",
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@@ -1080,7 +1113,7 @@
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+   "id": "18221ec4",
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@@ -1124,7 +1157,7 @@
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    "execution_count": 11,
-   "id": "9eebf739",
+   "id": "d4982f3a",
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@@ -1207,7 +1240,7 @@
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+   "id": "a146c2c5",
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@@ -1218,7 +1251,7 @@
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-   "id": "eed2466a",
+   "id": "2f7691d6",
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@@ -1320,7 +1353,7 @@
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-   "id": "71855b2c",
+   "id": "618a775c",
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@@ -1342,7 +1375,7 @@
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-   "id": "d1650ea8",
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@@ -1439,7 +1472,7 @@
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@@ -1465,7 +1498,7 @@
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-   "id": "bbb48a15",
+   "id": "701451d4",
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