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<title>9. Decision trees, overarching aims &#8212; Applied Data Analysis and Machine Learning</title>
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About the course
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@@ -116,7 +122,7 @@ const thebe_selector_output = ".output, .cell_output"
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Review of Statistics with Resampling Techniques and Linear Algebra
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@@ -133,7 +139,7 @@ const thebe_selector_output = ".output, .cell_output"
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From Regression to Support Vector Machines
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@@ -170,7 +176,7 @@ const thebe_selector_output = ".output, .cell_output"
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Decision trees, overarching aims</h1>
<!-- Table of contents -->
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<h2> Contents </h2>
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<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#basics-of-a-tree">
9.1. Basics of a tree
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#general-features">
9.2. General Features
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9.3. Building a tree, regression
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9.3.1. Making a tree
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9.3.2. Schematic Regression Procedure
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9.4. A Classification Tree
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9.4.1. Visualizing the Tree, Classification
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<a class="reference internal nav-link" href="#other-ways-of-visualizing-the-trees">
9.4.2. Other ways of visualizing the trees
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<a class="reference internal nav-link" href="#algorithms-for-setting-up-decision-trees">
9.5. Algorithms for Setting up Decision Trees
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<a class="reference internal nav-link" href="#the-cart-algorithm-for-classification">
9.5.1. The CART algorithm for Classification
</a>
</li>
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<a class="reference internal nav-link" href="#the-cart-algorithm-for-regression">
9.5.2. The CART algorithm for Regression
</a>
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<a class="reference internal nav-link" href="#computing-the-gini-index">
9.5.3. Computing the Gini index
</a>
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<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#simple-python-code-to-read-in-data-and-perform-classification">
9.5.4. Simple Python Code to read in Data and perform Classification
</a>
</li>
</ul>
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#entropy-and-the-id3-algorithm">
9.6. Entropy and the ID3 algorithm
</a>
<ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#cancer-data-again-now-with-decision-trees-and-other-methods">
9.6.1. Cancer Data again now with Decision Trees and other Methods
</a>
</li>
<li class="toc-h3 nav-item toc-entry">
<a class="reference internal nav-link" href="#another-example-the-moons-again">
9.6.2. Another example, the moons again
</a>
</li>
</ul>
</li>
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<a class="reference internal nav-link" href="#pros-and-cons-of-trees-pros">
9.7. Pros and cons of trees, pros
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<a class="reference internal nav-link" href="#disadvantages">
9.7.1. Disadvantages
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<div class="tex2jax_ignore mathjax_ignore section" id="decision-trees-overarching-aims">
@@ -564,9 +688,9 @@ predicting the target features of query instances is as follows:</p>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2nd degree coefficients:
zero power: 3.975510299261579
first power: -0.12345260617257443
second power: -0.0003043256065368937
zero power: 0.18790439176058887
first power: -0.014599964106338128
second power: 0.00010403373827253124
</pre></div>
</div>
<img alt="_images/chapter6_1_1.png" src="_images/chapter6_1_1.png" />
@@ -801,9 +925,9 @@ s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
<div class="cell_output docutils container">
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
<span class="ne">ModuleNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
<span class="o">&lt;</span><span class="n">ipython</span><span class="o">-</span><span class="nb">input</span><span class="o">-</span><span class="mi">2</span><span class="o">-</span><span class="mi">7</span><span class="n">c394b1e8b71</span><span class="o">&gt;</span> <span class="ow">in</span> <span class="o">&lt;</span><span class="n">module</span><span class="o">&gt;</span>
<span class="g g-Whitespace"> </span><span class="mi">7</span>
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="nn">Input In [2],</span> in <span class="ni">&lt;cell line: 9&gt;</span><span class="nt">()</span>
<span class="g g-Whitespace"> </span><span class="mi">6</span> <span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">export_graphviz</span>
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="ne">----&gt; </span><span class="mi">9</span> <span class="kn">from</span> <span class="nn">pydot</span> <span class="kn">import</span> <span class="n">graph_from_dot_data</span>
<span class="g g-Whitespace"> </span><span class="mi">10</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="g g-Whitespace"> </span><span class="mi">11</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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