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Morten Hjorth-Jensen
2023-11-21 06:16:54 +01:00
parent a854a3e9c0
commit 06b09eb681
173 changed files with 13894 additions and 3718 deletions
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@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
Week 45, Recurrent Neural Networks
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week46.html">
Week 46: Decision Trees, Ensemble methods and Random Forests
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week47.html">
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="project2.html">
Project 2 on Machine Learning, deadline November 13 (Midnight)
Project 2 on Machine Learning, deadline November 17 (Midnight)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="project3.html">
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
</a>
</li>
</ul>
@@ -1716,7 +1731,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.996738628265756
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9958946686888259
</pre></div>
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@@ -1733,7 +1748,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
</div>
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<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.00846262916105675
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.008142188979400687
</pre></div>
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@@ -1748,31 +1763,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[2.18321314e-02 4.63790586e-02 1.86599003e-02 2.57537966e-02
7.75301638e-03 2.41708096e-03 2.05388549e-02 2.24797183e-02
3.80669838e-02 1.77124395e-02 5.74437617e-02 8.91992985e-03
3.79831624e-02 1.33444711e-02 2.46753261e-02 2.04450975e-02
8.98180203e-02 1.21522960e-02 3.12747234e-03 1.31395784e-03
1.84447599e-03 2.96525482e-03 6.28909679e-03 1.52006777e-02
2.87135280e-03 2.40513177e-02 3.35417405e-02 5.92991719e-03
3.54379087e-02 7.18303628e-03 1.54601264e-02 2.15148810e-02
2.79754897e-03 4.12602928e-03 4.22227163e-02 3.09676156e-02
1.25713219e-02 2.32108713e-02 2.44657526e-02 1.05066388e-02
6.68324974e-02 2.97565845e-02 2.42484290e-02 1.89707309e-02
2.19461919e-02 1.41644629e-02 1.41226929e-02 5.23396766e-03
3.21530495e-03 3.66036618e-03 7.91408373e-03 3.18065689e-02
5.10582403e-02 6.76220793e-03 3.09797549e-02 1.01612033e-02
4.64257697e-02 1.98270777e-02 2.88088818e-02 5.94948363e-03
8.94501598e-03 4.64365518e-03 4.55438359e-02 3.34347894e-03
4.97761429e-03 2.71875845e-02 1.57316402e-02 4.15628391e-02
4.75979803e-02 8.77079389e-03 3.22623101e-03 2.53596681e-03
4.02206965e-02 3.06020683e-02 3.07080407e-02 9.75525377e-03
6.45380691e-02 2.66174067e-02 1.94727053e-03 4.82766482e-03
3.39313789e-03 5.00126617e-02 3.25794223e-02 3.97663980e-02
3.51267283e-02 4.43226747e-02 4.45976616e-03 2.86750237e-02
2.33197004e-02 9.78449688e-05 4.38688646e-02 2.86830766e-02
2.90763970e-02 9.24053124e-03 1.36970119e-02 6.97177697e-02
2.34728094e-02 2.31728952e-02 3.08484802e-03 6.25254477e-02]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.0476021 0.02689869 0.01088331 0.01783105 0.00544013 0.05110385
0.02900389 0.01629703 0.05594058 0.02527366 0.00657884 0.04127087
0.01925607 0.02221978 0.01212083 0.04919181 0.00745959 0.03110176
0.010203 0.0076995 0.00298213 0.01702968 0.04557362 0.03192124
0.06668218 0.0178392 0.00706728 0.0095239 0.00784983 0.05197707
0.01519861 0.0134093 0.00291822 0.00311528 0.02036289 0.01136976
0.0189559 0.04908155 0.01384493 0.01715895 0.01262581 0.00756465
0.00473818 0.00224783 0.01773579 0.03804636 0.03945128 0.01662346
0.05137822 0.00206124 0.06090176 0.01632212 0.01220987 0.06361921
0.00318122 0.00362359 0.03177421 0.06554078 0.00123144 0.01091059
0.04958045 0.00291334 0.01541622 0.00607264 0.05274561 0.007352
0.06263415 0.01593612 0.00853836 0.01006042 0.00223784 0.02106518
0.02410507 0.08294341 0.0043675 0.06502562 0.03422156 0.00213264
0.02365779 0.01883403 0.00683222 0.01848399 0.02930957 0.02161016
0.02746315 0.02774744 0.03591454 0.04814746 0.00568413 0.00215333
0.03631783 0.02866734 0.01684326 0.00953152 0.01001378 0.00119895
0.02603725 0.00127672 0.04770636 0.028797 ]
</pre></div>
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@@ -1841,15 +1848,15 @@ but now splitting the data into a training set and a test set.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.97864285 0.28134042 4.70594499 -0.58368727 0.70917314]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.02283241 0.15972118 3.84256187 1.89005305 -0.93145755]
Training R2
0.993658072083743
0.9959044445566834
Training MSE
0.012874822204495243
0.010349061754867921
Test R2
0.9945729062189713
0.9961996615568259
Test MSE
0.007472516848671787
0.008771887357306985
</pre></div>
</div>
</div>