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@@ -343,6 +343,11 @@ const thebe_selector_output = ".output, .cell_output"
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Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek43.html">
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Exercises week 43
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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@@ -1679,7 +1684,7 @@ theorem.</p>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Bootstrap Statistics :
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original bias std. error
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99.9716 15.1579 99.9734 0.151892
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100.052 15.0095 100.051 0.150055
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</pre></div>
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</div>
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</div>
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@@ -1904,7 +1909,9 @@ Error: 0.10398646080125035
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Bias^2: 0.1007711427354898
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Var: 0.0032153180657605116
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0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032
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Polynomial degree: 3
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 3
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Error: 0.06547790180152355
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Bias^2: 0.06208238634231949
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Var: 0.0033955154592040936
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@@ -1914,14 +1921,14 @@ Error: 0.06844519414009445
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Bias^2: 0.06453579006728324
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Var: 0.003909404072811226
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0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 5
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Polynomial degree: 5
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Error: 0.05227921801205686
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Bias^2: 0.0481872773043029
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Var: 0.004091940707753939
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0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844
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Polynomial degree: 6
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 6
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Error: 0.037813671417389005
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Bias^2: 0.033657685071527665
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Var: 0.00415598634586135
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@@ -1931,7 +1938,9 @@ Error: 0.02760977349102253
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Bias^2: 0.022999498260366312
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Var: 0.004610275230656212
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0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525
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Polynomial degree: 8
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 8
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Error: 0.017355848195593347
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Bias^2: 0.010331721306655127
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Var: 0.007024126888938232
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@@ -1951,21 +1960,21 @@ Error: 0.07160048164233104
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Bias^2: 0.014436800088904942
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Var: 0.05716368155342608
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0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 12
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Polynomial degree: 12
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Error: 0.11547777218872497
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Bias^2: 0.01628578269596628
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Var: 0.09919198949275869
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0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497
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Polynomial degree: 13
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 13
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Error: 0.22842468702219465
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Bias^2: 0.01975416527185249
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Var: 0.20867052175034223
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0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
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</pre></div>
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</div>
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<img alt="_images/week37_139_3.png" src="_images/week37_139_3.png" />
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<img alt="_images/week37_139_5.png" src="_images/week37_139_5.png" />
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</div>
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</div>
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</div>
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@@ -2302,12 +2311,12 @@ Mean squared error on test data: 129963.83146596
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Degree of polynomial: 3
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Mean squared error on training data: 9054.61775176
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Mean squared error on test data: 10572.87627342
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Degree of polynomial: 4
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Mean squared error on training data: 302.15313054
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Mean squared error on test data: 433.26292364
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 5
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 4
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Mean squared error on training data: 302.15313054
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Mean squared error on test data: 433.26292364
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Degree of polynomial: 5
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Mean squared error on training data: 3.64316192
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Mean squared error on test data: 7.23528337
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Degree of polynomial: 6
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@@ -2316,12 +2325,12 @@ Mean squared error on test data: 10.50427787
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Degree of polynomial: 7
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Mean squared error on training data: 0.47313680
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Mean squared error on test data: 1.53738247
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Degree of polynomial: 8
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Mean squared error on training data: 0.04926746
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Mean squared error on test data: 0.14629156
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 9
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 8
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Mean squared error on training data: 0.04926746
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Mean squared error on test data: 0.14629156
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Degree of polynomial: 9
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Mean squared error on training data: 0.02546675
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Mean squared error on test data: 0.11202337
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Degree of polynomial: 10
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@@ -2330,12 +2339,12 @@ Mean squared error on test data: 0.22467274
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Degree of polynomial: 11
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Mean squared error on training data: 0.01594452
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Mean squared error on test data: 1.07641937
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Degree of polynomial: 12
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Mean squared error on training data: 0.00805074
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Mean squared error on test data: 0.04295757
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 13
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 12
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Mean squared error on training data: 0.00805074
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Mean squared error on test data: 0.04295757
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Degree of polynomial: 13
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Mean squared error on training data: 0.00781918
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Mean squared error on test data: 0.56965674
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Degree of polynomial: 14
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@@ -2344,12 +2353,12 @@ Mean squared error on test data: 0.28443039
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Degree of polynomial: 15
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Mean squared error on training data: 0.00420072
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Mean squared error on test data: 568.47051432
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Degree of polynomial: 16
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Mean squared error on training data: 0.00325450
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Mean squared error on test data: 48.97630233
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 17
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 16
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Mean squared error on training data: 0.00325450
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Mean squared error on test data: 48.97630233
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Degree of polynomial: 17
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Mean squared error on training data: 0.00242954
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Mean squared error on test data: 2.52780600
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Degree of polynomial: 18
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@@ -2358,12 +2367,12 @@ Mean squared error on test data: 429.25695398
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Degree of polynomial: 19
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Mean squared error on training data: 0.00154853
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Mean squared error on test data: 239.97065359
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Degree of polynomial: 20
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Mean squared error on training data: 0.00140846
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Mean squared error on test data: 1350.24493666
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 21
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 20
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Mean squared error on training data: 0.00140846
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Mean squared error on test data: 1350.24493666
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Degree of polynomial: 21
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Mean squared error on training data: 0.00119688
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Mean squared error on test data: 1840.50530832
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Degree of polynomial: 22
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@@ -2372,12 +2381,12 @@ Mean squared error on test data: 1184.60929685
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Degree of polynomial: 23
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Mean squared error on training data: 0.00089193
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Mean squared error on test data: 3892.17483760
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Degree of polynomial: 24
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Mean squared error on training data: 0.00083355
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Mean squared error on test data: 1332.46736215
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 25
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 24
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Mean squared error on training data: 0.00083355
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Mean squared error on test data: 1332.46736215
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Degree of polynomial: 25
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Mean squared error on training data: 0.00079904
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Mean squared error on test data: 7577.76690383
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Degree of polynomial: 26
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@@ -2386,19 +2395,19 @@ Mean squared error on test data: 1079.36895644
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Degree of polynomial: 27
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Mean squared error on training data: 0.00068091
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Mean squared error on test data: 3207.25343155
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Degree of polynomial: 28
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Mean squared error on training data: 0.00063362
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Mean squared error on test data: 674.79633065
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 29
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 28
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Mean squared error on training data: 0.00063362
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Mean squared error on test data: 674.79633065
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Degree of polynomial: 29
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Mean squared error on training data: 0.00063866
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Mean squared error on test data: 3099.60342978
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</pre></div>
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</div>
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_87545/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94293/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
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plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
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/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_87545/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
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/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94293/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
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plt.plot(polynomial, np.log10(testerror), label='Test Error')
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</pre></div>
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</div>
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@@ -2483,7 +2492,7 @@ Mean squared error on test data: 3099.60342978
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_87545/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94293/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
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plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
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</pre></div>
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</div>
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