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Morten Hjorth-Jensen
2024-09-10 12:06:15 +02:00
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commit 4e5c11dfa3
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@@ -275,7 +275,34 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="week36.html">
Week 36: Linear Rgeression and Statistical interpretations
Week 36: Linear Regression and Statistical interpretations
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="exercisesweek37.html">
Exercises week 37
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week37.html">
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>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
Projects
</span>
</p>
<ul class="nav bd-sidenav">
<li class="toctree-l1">
<a class="reference internal" href="project1.html">
Project 1 on Machine Learning, deadline October 7 (midnight), 2024
</a>
</li>
</ul>
@@ -1609,7 +1636,7 @@ 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>0.9964486445275116
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9952537939995855
</pre></div>
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@@ -1626,7 +1653,7 @@ 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>0.008831941890485846
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.011208613520466846
</pre></div>
</div>
</div>
@@ -1641,31 +1668,23 @@ 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>[3.06442194e-02 4.73537890e-02 3.17755779e-02 1.51383260e-02
7.46749552e-02 6.37409975e-02 2.52554699e-02 4.98279090e-03
6.51178631e-02 7.74647981e-03 5.41761415e-03 2.97108525e-02
2.82566059e-02 2.35389684e-02 3.74830119e-02 1.60010693e-02
5.42765083e-02 1.15330788e-02 2.16632351e-02 1.46124943e-02
1.00902152e-02 2.58102999e-02 2.39990572e-02 1.04321941e-02
3.32351459e-02 5.63376422e-03 1.37416502e-02 1.21733307e-02
4.91402008e-03 2.73185968e-02 3.94556653e-02 1.74222022e-03
8.61562855e-03 2.13179053e-02 3.29487549e-02 5.99021575e-03
4.74063343e-03 1.32791346e-02 9.56466087e-03 3.74303070e-03
2.74070824e-02 5.52656770e-03 1.95782166e-02 4.32740721e-02
5.08750220e-02 1.46260797e-02 2.78058232e-02 6.72219105e-03
9.68078357e-03 3.62788541e-02 5.12122786e-03 2.09047191e-02
5.08323973e-02 4.05073207e-02 3.21117128e-02 4.76187240e-04
8.71538320e-03 1.54428380e-03 3.46608732e-02 7.51681181e-03
9.49622615e-03 7.23177156e-05 2.76887029e-02 3.93356853e-02
3.23505507e-02 1.98625331e-02 8.86557766e-03 2.82168579e-03
5.88253432e-02 1.67851352e-02 4.99217800e-02 1.89971681e-03
6.65367685e-02 3.13641587e-03 8.97992238e-04 3.55757089e-02
4.72545392e-02 1.95980855e-02 1.51198558e-02 3.43246775e-03
5.17748443e-02 1.65904730e-02 3.62201698e-03 1.20488808e-02
6.72793290e-02 1.72664028e-02 5.25325161e-03 7.70435575e-03
4.60004008e-02 2.60656897e-04 1.69087404e-02 1.01813007e-02
3.73223692e-02 1.89954169e-02 3.30764357e-02 6.71384474e-02
1.58314173e-02 2.04242885e-02 4.47734350e-02 5.36097931e-02]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.05040878 0.02601643 0.01922269 0.05006037 0.02572685 0.10595991
0.04487298 0.00334047 0.00330046 0.00606382 0.02500488 0.03247316
0.01897462 0.0241039 0.0606958 0.00472276 0.01756114 0.06536971
0.02809972 0.04955942 0.00956827 0.00667611 0.02576358 0.04216532
0.04808723 0.01625794 0.00282226 0.00220013 0.00017733 0.0211429
0.02207054 0.02156196 0.0694226 0.01119738 0.0041148 0.01783096
0.0062202 0.03317599 0.02032056 0.00798909 0.06901081 0.01353638
0.01863203 0.01179128 0.01178857 0.00634299 0.01793261 0.00018053
0.13055762 0.02441422 0.05029018 0.0253208 0.01979808 0.02693015
0.05336637 0.01373484 0.09291806 0.00168745 0.04588592 0.01013849
0.04018985 0.03887801 0.03033791 0.01811279 0.02540212 0.02980537
0.02784266 0.03158013 0.01060492 0.01620955 0.00942574 0.0043587
0.02651857 0.00053001 0.0337609 0.01131771 0.00023813 0.02091662
0.01315875 0.00434043 0.04161572 0.05045 0.0121289 0.01532738
0.02334754 0.01206221 0.00930146 0.03244944 0.00702721 0.02576685
0.05224117 0.0262517 0.02946852 0.09604976 0.01406777 0.02183817
0.0164974 0.02322594 0.04238763 0.00647029]
</pre></div>
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</div>
@@ -1734,15 +1753,15 @@ but now splitting the data into a training set and a test set.</p>
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<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.06926365 -1.13588335 10.35444257 -8.67801834 4.51542953]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.82079885 2.45560415 -4.73595198 14.38102552 -7.04838148]
Training R2
0.9951502749739212
0.9952183728736417
Training MSE
0.011265488125148788
0.009338082195270294
Test R2
0.9923539830272697
0.9969461173312454
Test MSE
0.009522910538005715
0.008043811612683473
</pre></div>
</div>
</div>