updating book

2023-09-18 12:44:58 +02:00
parent 409e8ba58e
commit 83ef9e4213
18 changed files with 866 additions and 856 deletions
@@ -5,7 +5,7 @@
  <head>
    <meta charset="utf-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>Week 37: Statitsitcal interpretations and Resampling Methods &#8212; Applied Data Analysis and Machine Learning</title>
+    <title>Week 37: Statistical interpretations and Resampling Methods &#8212; Applied Data Analysis and Machine Learning</title>
    
  <link href="_static/css/theme.css" rel="stylesheet">
  <link href="_static/css/index.ff1ffe594081f20da1ef19478df9384b.css" rel="stylesheet">
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
    <link rel="index" title="Index" href="genindex.html" />
    <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Project 1 on Machine Learning, deadline October 9 (midnight), 2023" href="project1.html" />
+    <link rel="next" title="Exercises week 38" href="exercisesweek38.html" />
    <link rel="prev" title="Exercises week 37" href="exercisesweek37.html" />
    <meta name="viewport" content="width=device-width, initial-scale=1" />
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@@ -285,7 +285,17 @@ const thebe_selector_output = ".output, .cell_output"
 </li>
 <li class="toctree-l1 current active">
  <a class="current reference internal" href="#">
-   Week 37: Statitsitcal interpretations and Resampling Methods
+   Week 37: Statistical interpretations and Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek38.html">
+   Exercises week 38
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week38.html">
+   Week38: Logistic Regression and Optimization
  </a>
 </li>
 </ul>
@@ -657,7 +667,7 @@ const thebe_selector_output = ".output, .cell_output"
        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
            <!-- Table of contents that is only displayed when printing the page -->
            <div id="jb-print-docs-body" class="onlyprint">
-                <h1>Week 37: Statitsitcal interpretations and Resampling Methods</h1>
+                <h1>Week 37: Statistical interpretations and Resampling Methods</h1>
                <!-- Table of contents -->
                <div id="print-main-content">
                    <div id="jb-print-toc">
@@ -954,10 +964,10 @@ const thebe_selector_output = ".output, .cell_output"
                
  <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
 doconce format html week37.do.txt --no_mako -->
-<!-- dom:TITLE: Week 37: Statitsitcal interpretations and Resampling Methods --><div class="tex2jax_ignore mathjax_ignore section" id="week-37-statitsitcal-interpretations-and-resampling-methods">
-<h1>Week 37: Statitsitcal interpretations and Resampling Methods<a class="headerlink" href="#week-37-statitsitcal-interpretations-and-resampling-methods" title="Permalink to this headline">¶</a></h1>
+<!-- dom:TITLE: Week 37: Statistical interpretations and Resampling Methods --><div class="tex2jax_ignore mathjax_ignore section" id="week-37-statistical-interpretations-and-resampling-methods">
+<h1>Week 37: Statistical interpretations and Resampling Methods<a class="headerlink" href="#week-37-statistical-interpretations-and-resampling-methods" title="Permalink to this headline">¶</a></h1>
 <p><strong>Morten Hjorth-Jensen</strong>, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University</p>
-<p>Date: <strong>Sep 14, 2023</strong></p>
+<p>Date: <strong>Sep 18, 2023</strong></p>
 <p>Copyright 1999-2023, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license</p>
 <!-- todo add link to videos and add link to Van Wieringens notes --><div class="section" id="plans-for-week-37">
 <h2>Plans for week 37<a class="headerlink" href="#plans-for-week-37" title="Permalink to this headline">¶</a></h2>
@@ -1767,7 +1777,7 @@ theorem.</p>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Bootstrap Statistics :
 original           bias      std. error
- 99.9713   14.943         99.972        0.149583
+  99.971  15.2789        99.9715        0.152907
 </pre></div>
 </div>
 </div>
@@ -2002,14 +2012,14 @@ Error: 0.06844519414009445
 Bias^2: 0.06453579006728322
 Var: 0.003909404072811221
 0.06844519414009445 &gt;= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 5
+Polynomial degree: 5
 Error: 0.05227921801205679
 Bias^2: 0.04818727730430286
 Var: 0.004091940707753925
 0.05227921801205679 &gt;= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679
-Polynomial degree: 6
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 6
 Error: 0.03781367141738902
 Bias^2: 0.03365768507152769
 Var: 0.0041559863458613296
@@ -2029,14 +2039,14 @@ Error: 0.026605727637184558
 Bias^2: 0.010018312644139219
 Var: 0.016587414993045335
 0.026605727637184558 &gt;= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554
-Polynomial degree: 10
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 10
 Error: 0.021592704588021178
 Bias^2: 0.010516485576646504
 Var: 0.01107621901137467
 0.021592704588021178 &gt;= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 11
+Polynomial degree: 11
 Error: 0.07160048164232538
 Bias^2: 0.014436800088896381
 Var: 0.05716368155342902
@@ -2473,12 +2483,12 @@ Mean squared error on test data: 238.16356503
 Degree of polynomial:  20
 Mean squared error on training data: 0.00140849
 Mean squared error on test data: 1345.68592431
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial:  21
+Degree of polynomial:  21
 Mean squared error on training data: 0.00119699
 Mean squared error on test data: 1836.21110005
-Degree of polynomial:  22
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial:  22
 Mean squared error on training data: 0.00092904
 Mean squared error on test data: 1182.64316482
 Degree of polynomial:  23
@@ -2493,12 +2503,12 @@ Mean squared error on test data: 7697.35412147
 Degree of polynomial:  26
 Mean squared error on training data: 0.00075597
 Mean squared error on test data: 1078.81597834
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial:  27
+Degree of polynomial:  27
 Mean squared error on training data: 0.00068088
 Mean squared error on test data: 3189.20355156
-Degree of polynomial:  28
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial:  28
 Mean squared error on training data: 0.00063364
 Mean squared error on test data: 692.24085321
 Degree of polynomial:  29
@@ -2506,9 +2516,9 @@ Mean squared error on training data: 0.00063862
 Mean squared error on test data: 3073.63180447
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31560/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10727/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
  plt.plot(polynomial, np.log10(trainingerror), label=&#39;Training Error&#39;)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31560/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10727/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
  plt.plot(polynomial, np.log10(testerror), label=&#39;Test Error&#39;)
 </pre></div>
 </div>
@@ -2593,7 +2603,7 @@ Mean squared error on test data: 3073.63180447
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31560/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10727/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label=&#39;Test Error&#39;)
 </pre></div>
 </div>
@@ -2990,10 +3000,10 @@ using the <strong>standardscaler</strong> functionality of the library
            <p class="prev-next-title">Exercises week 37</p>
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+    <a class='right-next' id="next-link" href="exercisesweek38.html" title="next page">
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        <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</p>
+        <p class="prev-next-title">Exercises week 38</p>
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