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<!-- navigation toc: --> <li><a href="._week44-bs001.html#___sec0" style="font-size: 80%;">Decision trees, overarching aims</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs002.html#___sec1" 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-bs005.html#___sec4" style="font-size: 80%;">Decision trees and Regression</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs006.html#___sec5" style="font-size: 80%;">Building a tree, regression</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs007.html#___sec6" style="font-size: 80%;">A top-down approach, recursive binary splitting</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs008.html#___sec7" style="font-size: 80%;">Making a tree</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs009.html#___sec8" style="font-size: 80%;">Pruning the tree</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs010.html#___sec9" style="font-size: 80%;">Cost complexity pruning</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs012.html#___sec11" style="font-size: 80%;">A Classification Tree</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs014.html#___sec13" style="font-size: 80%;">Classification tree, how to split nodes</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs015.html#___sec14" style="font-size: 80%;">Visualizing the Tree, Classification</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs016.html#___sec15" style="font-size: 80%;">Visualizing the Tree, The Moons</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs017.html#___sec16" style="font-size: 80%;">Algorithms for Setting up Decision Trees</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs018.html#___sec17" style="font-size: 80%;">The CART algorithm for Classification</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs019.html#___sec18" style="font-size: 80%;">The CART algorithm for Regression</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs020.html#___sec19" style="font-size: 80%;">Computing the Gini index</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs021.html#___sec20" style="font-size: 80%;">Simple Python Code to read in Data and perform Classification</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs022.html#___sec21" style="font-size: 80%;">Computing the Gini Factor</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs023.html#___sec22" style="font-size: 80%;">Entropy and the ID3 algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs024.html#___sec23" style="font-size: 80%;">Implementing the ID3 Algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs025.html#___sec24" style="font-size: 80%;">Cancer Data again now with Decision Trees and other Methods</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs026.html#___sec25" style="font-size: 80%;">Another example, the moons again</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs027.html#___sec26" style="font-size: 80%;">Playing around with regions</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs028.html#___sec27" style="font-size: 80%;">Regression trees</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs029.html#___sec28" style="font-size: 80%;">Final regressor code</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs030.html#___sec29" style="font-size: 80%;">Pros and cons of trees, pros</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs031.html#___sec30" style="font-size: 80%;">Disadvantages</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs032.html#___sec31" style="font-size: 80%;">Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs033.html#___sec32" style="font-size: 80%;">An Overview of Ensemble Methods</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs034.html#___sec33" style="font-size: 80%;">Bagging</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs035.html#___sec34" style="font-size: 80%;">More bagging</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs036.html#___sec35" style="font-size: 80%;">Simple Voting Example, head or tail</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs037.html#___sec36" style="font-size: 80%;">Using the Voting Classifier</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
<!-- navigation toc: --> <li><a href="#___sec38" style="font-size: 80%;">Bagging Examples</a></li>
<!-- navigation toc: --> <li><a href="._week44-bs040.html#___sec39" style="font-size: 80%;">Making your own Bootstrap: Changing the Level of the Decision Tree</a></li>
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<h2 id="___sec38" class="anchor">Bagging Examples </h2>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> BaggingClassifier
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
bag_clf <span style="color: #666666">=</span> BaggingClassifier(
DecisionTreeClassifier(random_state<span style="color: #666666">=42</span>), n_estimators<span style="color: #666666">=500</span>,
max_samples<span style="color: #666666">=100</span>, bootstrap<span style="color: #666666">=</span><span style="color: #008000">True</span>, n_jobs<span style="color: #666666">=-1</span>, random_state<span style="color: #666666">=42</span>)
bag_clf<span style="color: #666666">.</span>fit(X_train, y_train)
y_pred <span style="color: #666666">=</span> bag_clf<span style="color: #666666">.</span>predict(X_test)
</pre></div>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.metrics</span> <span style="color: #008000; font-weight: bold">import</span> accuracy_score
<span style="color: #008000; font-weight: bold">print</span>(accuracy_score(y_test, y_pred))
</pre></div>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>tree_clf <span style="color: #666666">=</span> DecisionTreeClassifier(random_state<span style="color: #666666">=42</span>)
tree_clf<span style="color: #666666">.</span>fit(X_train, y_train)
y_pred_tree <span style="color: #666666">=</span> tree_clf<span style="color: #666666">.</span>predict(X_test)
<span style="color: #008000; font-weight: bold">print</span>(accuracy_score(y_test, y_pred_tree))
</pre></div>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">matplotlib.colors</span> <span style="color: #008000; font-weight: bold">import</span> ListedColormap
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plot_decision_boundary</span>(clf, X, y, axes<span style="color: #666666">=</span>[<span style="color: #666666">-1.5</span>, <span style="color: #666666">2.5</span>, <span style="color: #666666">-1</span>, <span style="color: #666666">1.5</span>], alpha<span style="color: #666666">=0.5</span>, contour<span style="color: #666666">=</span><span style="color: #008000">True</span>):
x1s <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(axes[<span style="color: #666666">0</span>], axes[<span style="color: #666666">1</span>], <span style="color: #666666">100</span>)
x2s <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(axes[<span style="color: #666666">2</span>], axes[<span style="color: #666666">3</span>], <span style="color: #666666">100</span>)
x1, x2 <span style="color: #666666">=</span> np<span style="color: #666666">.</span>meshgrid(x1s, x2s)
X_new <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[x1<span style="color: #666666">.</span>ravel(), x2<span style="color: #666666">.</span>ravel()]
y_pred <span style="color: #666666">=</span> clf<span style="color: #666666">.</span>predict(X_new)<span style="color: #666666">.</span>reshape(x1<span style="color: #666666">.</span>shape)
custom_cmap <span style="color: #666666">=</span> ListedColormap([<span style="color: #BA2121">&#39;#fafab0&#39;</span>,<span style="color: #BA2121">&#39;#9898ff&#39;</span>,<span style="color: #BA2121">&#39;#a0faa0&#39;</span>])
plt<span style="color: #666666">.</span>contourf(x1, x2, y_pred, alpha<span style="color: #666666">=0.3</span>, cmap<span style="color: #666666">=</span>custom_cmap)
<span style="color: #008000; font-weight: bold">if</span> contour:
custom_cmap2 <span style="color: #666666">=</span> ListedColormap([<span style="color: #BA2121">&#39;#7d7d58&#39;</span>,<span style="color: #BA2121">&#39;#4c4c7f&#39;</span>,<span style="color: #BA2121">&#39;#507d50&#39;</span>])
plt<span style="color: #666666">.</span>contour(x1, x2, y_pred, cmap<span style="color: #666666">=</span>custom_cmap2, alpha<span style="color: #666666">=0.8</span>)
plt<span style="color: #666666">.</span>plot(X[:, <span style="color: #666666">0</span>][y<span style="color: #666666">==0</span>], X[:, <span style="color: #666666">1</span>][y<span style="color: #666666">==0</span>], <span style="color: #BA2121">&quot;yo&quot;</span>, alpha<span style="color: #666666">=</span>alpha)
plt<span style="color: #666666">.</span>plot(X[:, <span style="color: #666666">0</span>][y<span style="color: #666666">==1</span>], X[:, <span style="color: #666666">1</span>][y<span style="color: #666666">==1</span>], <span style="color: #BA2121">&quot;bs&quot;</span>, alpha<span style="color: #666666">=</span>alpha)
plt<span style="color: #666666">.</span>axis(axes)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">r&quot;$x_1$&quot;</span>, fontsize<span style="color: #666666">=18</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">r&quot;$x_2$&quot;</span>, fontsize<span style="color: #666666">=18</span>, rotation<span style="color: #666666">=0</span>)
plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">11</span>,<span style="color: #666666">4</span>))
plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">121</span>)
plot_decision_boundary(tree_clf, X, y)
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Decision Tree&quot;</span>, fontsize<span style="color: #666666">=14</span>)
plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">122</span>)
plot_decision_boundary(bag_clf, X, y)
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Decision Trees with Bagging&quot;</span>, fontsize<span style="color: #666666">=14</span>)
save_fig(<span style="color: #BA2121">&quot;baggingtree&quot;</span>)
plt<span style="color: #666666">.</span>show()
</pre></div>
<p>
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