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@@ -257,6 +257,9 @@
 <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>
 
+<li class="toctree-l1"><a class="reference internal" href="week48.html">Week 48: Gradient boosting  and summary of course</a></li>
+<li class="toctree-l1"><a class="reference internal" href="exercisesweek48.html">Exercises week 48</a></li>
+
 </ul>
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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@@ -730,7 +733,7 @@ Thereafter we wish to apply it to data which were not included in the training.
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+<img alt="_images/01d44d8b3777894a56df96cdebd9411c2a9b9484e40698be0bb84652c7d0f7cf.png" src="_images/01d44d8b3777894a56df96cdebd9411c2a9b9484e40698be0bb84652c7d0f7cf.png" />
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 <p>This example serves several aims. It allows us to demonstrate several
@@ -815,7 +818,7 @@ to be dominated by outliers.</p>
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+<img alt="_images/326bc2bdd25593d0947bbe11be557f7c976fd425b872153c8c8317ebbb51c5ae.png" src="_images/326bc2bdd25593d0947bbe11be557f7c976fd425b872153c8c8317ebbb51c5ae.png" />
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 <p>Depending on the parameter in front of the normal distribution, we may
@@ -862,16 +865,16 @@ 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: 
- [1.97386121]
+ [2.00949975]
 Coefficient beta : 
- [[5.12574106]]
-Mean squared error: 0.20
-Variance score: 0.92
-Mean squared log error: 0.01
-Mean absolute error: 0.36
+ [[5.07244691]]
+Mean squared error: 0.30
+Variance score: 0.88
+Mean squared log error: 0.02
+Mean absolute error: 0.42
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+<img alt="_images/b39b8ed8a4aed5f4a94d2137c267e12d64d23f66d1c50c3e3ad1ae8cd8b9ff5e.png" src="_images/b39b8ed8a4aed5f4a94d2137c267e12d64d23f66d1c50c3e3ad1ae8cd8b9ff5e.png" />
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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
@@ -967,8 +970,8 @@ a linear <span class="math notranslate nohighlight">\(x\)</span>-dependence we s
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.004999999999999993
+<img alt="_images/99bd4e87bbb54a5fb018ef916c872e3158711414d535c467a50d947ace53c954.png" src="_images/99bd4e87bbb54a5fb018ef916c872e3158711414d535c467a50d947ace53c954.png" />
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.005000000000000009
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