small update
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@@ -55,6 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
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<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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<link rel="index" title="Index" href="genindex.html" />
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<link rel="search" title="Search" href="search.html" />
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<link rel="next" title="Exercises week 36" href="exercisesweek36.html" />
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<link rel="prev" title="Exercises week 35" href="exercisesweek35.html" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="docsearch:language" content="None">
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@@ -267,6 +268,16 @@ const thebe_selector_output = ".output, .cell_output"
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Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
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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="exercisesweek36.html">
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Exercises week 36
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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="week36.html">
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Week 36: Statistical interpretation of Linear Regression and Resampling techniques
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</a>
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</li>
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</ul>
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</div>
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@@ -1613,7 +1624,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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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 stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9949347794612255
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9959033816551833
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</pre></div>
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</div>
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</div>
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@@ -1630,7 +1641,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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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 stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.01028995427580139
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.009290411029763584
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</pre></div>
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</div>
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</div>
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@@ -1645,31 +1656,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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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 stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[3.27961873e-03 1.75320666e-02 3.97840267e-02 1.41369795e-02
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1.12890007e-02 3.55765644e-02 1.34914442e-03 9.92463154e-03
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3.86416637e-02 8.40062254e-03 1.58912508e-02 7.41091395e-02
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2.28266792e-02 1.29662593e-03 1.47977394e-02 1.48877744e-02
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3.81874188e-02 7.92322465e-02 3.43012535e-02 1.47732343e-02
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2.59567145e-02 1.77816763e-02 1.07826171e-02 3.45241645e-02
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3.01752962e-02 4.99180856e-03 2.49838167e-04 4.38432672e-03
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5.93034176e-03 2.54644646e-02 1.76834212e-02 1.85785878e-02
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3.70827971e-02 9.47008799e-03 6.04008203e-02 2.45865699e-02
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2.43113284e-03 2.65643756e-02 1.40768645e-01 4.45840226e-03
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1.23365145e-02 3.49001938e-02 3.65693278e-02 2.08966600e-02
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1.45112450e-02 1.27524966e-02 7.26193455e-02 3.00129543e-02
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5.95594445e-03 1.06826023e-02 1.32609560e-02 1.42729886e-02
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2.87131961e-02 6.39892216e-02 4.19650622e-02 2.78272242e-02
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1.19023414e-02 2.32474290e-02 5.36904006e-02 7.48259891e-03
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2.74997869e-02 3.08549564e-02 4.12211324e-02 1.06229862e-02
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4.41816468e-02 5.87890188e-04 4.41486312e-02 1.41083418e-02
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5.35759569e-03 2.89869600e-02 2.20517254e-02 2.65534935e-02
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1.04481443e-02 2.47355725e-02 1.00634741e-02 2.73185363e-02
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1.13375152e-02 4.40236659e-02 1.20669787e-02 7.63258368e-02
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1.13208672e-04 1.15746031e-02 9.91094169e-03 4.09789363e-02
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5.98604688e-02 1.47082726e-02 1.54957609e-02 3.71921767e-02
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2.85688243e-03 7.15752533e-04 4.77553346e-02 2.33370315e-03
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3.30638512e-05 7.19284552e-02 5.16049171e-02 7.79000942e-03
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2.70465373e-02 5.84696315e-02 5.47099765e-02 6.36462687e-02]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.02126934 0.07602623 0.0014937 0.01294617 0.05421908 0.02237669
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0.0026081 0.00564956 0.00399247 0.05763394 0.00040953 0.06861085
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0.0098629 0.01199845 0.0097577 0.03260201 0.020964 0.01933058
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0.02255062 0.01801774 0.04039087 0.00472303 0.01003763 0.01487742
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0.05554042 0.00886682 0.05110883 0.02944194 0.00806407 0.01028231
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0.03613949 0.03352185 0.0512238 0.01525206 0.00660801 0.01073938
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0.06353697 0.00700232 0.0391902 0.08741274 0.01227458 0.01049472
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0.04691549 0.00963223 0.0143088 0.05177527 0.00850988 0.01121347
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0.02768957 0.02259051 0.02233576 0.01322543 0.02143332 0.01400329
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0.00102864 0.01322099 0.00611932 0.01011376 0.13281267 0.00684221
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0.05358851 0.02232779 0.00695738 0.03054765 0.00554475 0.05748797
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0.03507211 0.00563446 0.03123832 0.00033779 0.01122997 0.1098906
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0.07003926 0.03718926 0.0695405 0.00605451 0.0456042 0.00477722
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0.01224109 0.01072866 0.04273116 0.01873409 0.02903947 0.01927709
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0.00819724 0.00628788 0.00086553 0.02341603 0.0525063 0.03546779
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0.03012368 0.05069808 0.00327082 0.00517074 0.00071305 0.01194406
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0.05454172 0.02480935 0.00577016 0.02925853]
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</pre></div>
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</div>
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</div>
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@@ -1738,15 +1741,15 @@ but now splitting the data into a training set and a test set.</p>
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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 stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.96667129 1.00622755 0.25455904 7.36570403 -3.55965719]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.04860436 -0.39444293 5.97533203 -0.78980112 0.09575221]
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Training R2
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0.9967912002709148
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0.9960913291755783
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Training MSE
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0.007098683295819775
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0.008823125370709272
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Test R2
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0.9957763445154583
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0.9906601091977738
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Test MSE
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0.008754614401844201
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0.01969004537138309
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</pre></div>
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</div>
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</div>
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@@ -4221,6 +4224,13 @@ C(\boldsymbol{X},\boldsymbol{\beta})=\frac{1}{n}\left\{(\boldsymbol{y}-\boldsymb
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<p class="prev-next-title">Exercises week 35</p>
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</div>
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<div class="prev-next-info">
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<p class="prev-next-subtitle">next</p>
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<p class="prev-next-title">Exercises week 36</p>
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</div>
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