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Morten Hjorth-Jensen
2024-11-17 15:55:37 +01:00
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@@ -254,6 +254,9 @@
<li class="toctree-l1"><a class="reference internal" href="week44.html">Week 44, Convolutional Neural Networks (CNN)</a></li>
<li class="toctree-l1"><a class="reference internal" href="week45.html">Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)</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</a></li>
<li class="toctree-l1"><a class="reference internal" href="exercisesweek47.html">Exercise week 47</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
<ul class="nav bd-sidenav">
@@ -727,7 +730,7 @@ Thereafter we wish to apply it to data which were not included in the training.
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<p>This example serves several aims. It allows us to demonstrate several
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<p>Depending on the parameter in front of the normal distribution, we may
@@ -859,16 +862,16 @@ example of the functionality of <strong>Scikit-Learn</strong>.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The intercept alpha:
[2.04161185]
[1.97386121]
Coefficient beta :
[[4.82942403]]
Mean squared error: 0.25
Variance score: 0.87
[[5.12574106]]
Mean squared error: 0.20
Variance score: 0.92
Mean squared log error: 0.01
Mean absolute error: 0.40
Mean absolute error: 0.36
</pre></div>
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<p>The function <strong>coef</strong> gives us the parameter <span class="math notranslate nohighlight">\(\beta\)</span> of our fit while <strong>intercept</strong> yields
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.004999999999999993
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