mhjensen
281 lines
16 KiB
HTML
281 lines
16 KiB
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<a class="navbar-brand" href="week44-bs.html">Week 44: From Decision Trees to Bagging methods</a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._week44-bs001.html#___sec0" style="font-size: 80%;">Overview of week 44</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs002.html#___sec1" style="font-size: 80%;">Thursday</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs003.html#___sec2" style="font-size: 80%;">Decision trees, overarching aims</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs004.html#___sec3" style="font-size: 80%;">Basics of a tree</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs005.html#___sec4" style="font-size: 80%;">A Sketch of a Tree, Regression problem</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs006.html#___sec5" style="font-size: 80%;">A Sketch of a Tree, Classification problem</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs007.html#___sec6" style="font-size: 80%;">A typical Decision Tree with its pertinent Jargon, Classification Problem</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs008.html#___sec7" style="font-size: 80%;">General Features</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs009.html#___sec8" style="font-size: 80%;">How do we set it up?</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs010.html#___sec9" style="font-size: 80%;">Decision trees and Regression</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs011.html#___sec10" style="font-size: 80%;">Building a tree, regression</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs012.html#___sec11" style="font-size: 80%;">A top-down approach, recursive binary splitting</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs013.html#___sec12" style="font-size: 80%;">Making a tree</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs014.html#___sec13" style="font-size: 80%;">Pruning the tree</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs015.html#___sec14" style="font-size: 80%;">Cost complexity pruning</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs016.html#___sec15" style="font-size: 80%;">Schematic Regression Procedure</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs017.html#___sec16" style="font-size: 80%;">A Classification Tree</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs018.html#___sec17" style="font-size: 80%;">Growing a classification tree</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs019.html#___sec18" style="font-size: 80%;">Classification tree, how to split nodes</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs020.html#___sec19" style="font-size: 80%;">Visualizing the Tree, Classification</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs021.html#___sec20" style="font-size: 80%;">Visualizing the Tree, The Moons</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs022.html#___sec21" style="font-size: 80%;">Other ways of visualizing the trees</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs023.html#___sec22" style="font-size: 80%;">Printing out as text</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs024.html#___sec23" style="font-size: 80%;">Algorithms for Setting up Decision Trees</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs025.html#___sec24" style="font-size: 80%;">The CART algorithm for Classification</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs026.html#___sec25" style="font-size: 80%;">The CART algorithm for Regression</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs027.html#___sec26" style="font-size: 80%;">Computing the Gini index</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs028.html#___sec27" style="font-size: 80%;">Simple Python Code to read in Data and perform Classification</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs029.html#___sec28" style="font-size: 80%;">Computing the Gini Factor</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs030.html#___sec29" style="font-size: 80%;">Entropy and the ID3 algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs031.html#___sec30" style="font-size: 80%;">Cancer Data again now with Decision Trees and other Methods</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs032.html#___sec31" style="font-size: 80%;">Another example, the moons again</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs033.html#___sec32" style="font-size: 80%;">Playing around with regions</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs034.html#___sec33" style="font-size: 80%;">Regression trees</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs035.html#___sec34" style="font-size: 80%;">Final regressor code</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs036.html#___sec35" style="font-size: 80%;">Pros and cons of trees, pros</a></li>
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<!-- navigation toc: --> <li><a href="#___sec36" style="font-size: 80%;">Disadvantages</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs038.html#___sec37" style="font-size: 80%;">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs039.html#___sec38" style="font-size: 80%;">An Overview of Ensemble Methods</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs040.html#___sec39" style="font-size: 80%;">Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs041.html#___sec40" style="font-size: 80%;">More bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs042.html#___sec41" style="font-size: 80%;">Simple Voting Example, head or tail</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs043.html#___sec42" style="font-size: 80%;">Using the Voting Classifier</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs044.html#___sec43" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs045.html#___sec44" style="font-size: 80%;">Bagging Examples</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs046.html#___sec45" style="font-size: 80%;">Making your own Bootstrap: Changing the Level of the Decision Tree</a></li>
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</ul>
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</li>
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</ul>
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</div>
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</div>
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0037"></a>
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<!-- !split -->
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<h2 id="___sec36" class="anchor">Disadvantages </h2>
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<ul>
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<li> Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches</li>
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<li> If continuous features are used the tree may become quite large and hence less interpretable</li>
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<li> Decision trees are prone to overfit the training data and hence do not well generalize the data if no stopping criteria or improvements like pruning, boosting or bagging are implemented</li>
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<li> Small changes in the data may lead to a completely different tree. This issue can be addressed by using ensemble methods like bagging, boosting or random forests</li>
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<li> Unbalanced datasets where some target feature values occur much more frequently than others may lead to biased trees since the frequently occurring feature values are preferred over the less frequently occurring ones.</li>
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<li> If the number of features is relatively large (high dimensional) and the number of instances is relatively low, the tree might overfit the data</li>
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<li> Features with many levels may be preferred over features with less levels since for them it is <em>more easy</em> to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain</li>
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</ul>
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However, by aggregating many decision trees, using methods like
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bagging, random forests, and boosting, the predictive performance of
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trees can be substantially improved.
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<p>
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