327 lines
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327 lines
23 KiB
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'Classification Problem',
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2,
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('General Features', 2, None, '___sec2'),
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('How do we set it up?', 2, None, '___sec3'),
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('Building a tree, regression', 2, None, '___sec5'),
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2,
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None,
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('Making a tree', 2, None, '___sec7'),
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('Growing a classification tree', 2, None, '___sec12'),
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('Classification tree, how to split nodes', 2, None, '___sec13'),
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('Visualizing the Tree, Classification', 2, None, '___sec14'),
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('Visualizing the Tree, The Moons', 2, None, '___sec15'),
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('Algorithms for Setting up Decision Trees', 2, None, '___sec16'),
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('The CART algorithm for Classification', 2, None, '___sec17'),
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('The CART algorithm for Regression', 2, None, '___sec18'),
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('Entropy and the ID3 algorithm', 2, None, '___sec22'),
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('Implementing the ID3 Algorithm', 2, None, '___sec23'),
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('Cancer Data again now with Decision Trees and other Methods',
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2,
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None,
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'___sec24'),
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('Another example, the moons again', 2, None, '___sec25'),
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('Playing around with regions', 2, None, '___sec26'),
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('Regression trees', 2, None, '___sec27'),
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('Final regressor code', 2, None, '___sec28'),
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('Pros and cons of trees, pros', 2, None, '___sec29'),
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('Disadvantages', 2, None, '___sec30'),
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('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
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'Boosting, Meet the Jungle of Methods',
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2,
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None,
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'___sec31'),
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('An Overview of Ensemble Methods', 2, None, '___sec32'),
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('Bagging', 2, None, '___sec33'),
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('More bagging', 2, None, '___sec34'),
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('Simple Voting Example, head or tail', 2, None, '___sec35'),
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('Using the Voting Classifier', 2, None, '___sec36'),
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('Please, not the moons again! Voting and Bagging',
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2,
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None,
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'___sec37'),
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('Bagging Examples', 2, None, '___sec38'),
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('Making your own Bootstrap: Changing the Level of the Decision '
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2,
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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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<!-- navigation toc: --> <li><a href="._week44-bs001.html#___sec0" style="font-size: 80%;">Decision trees, overarching aims</a></li>
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<!-- 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-bs003.html#___sec2" style="font-size: 80%;">General Features</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs004.html#___sec3" style="font-size: 80%;">How do we set it up?</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>
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<!-- navigation toc: --> <li><a href="._week44-bs006.html#___sec5" style="font-size: 80%;">Building a tree, regression</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs007.html#___sec6" style="font-size: 80%;">A top-down approach, recursive binary splitting</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs008.html#___sec7" style="font-size: 80%;">Making a tree</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs009.html#___sec8" style="font-size: 80%;">Pruning the tree</a></li>
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<!-- 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-bs011.html#___sec10" style="font-size: 80%;">Schematic Regression Procedure</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-bs013.html#___sec12" style="font-size: 80%;">Growing 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>
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<!-- navigation toc: --> <li><a href="._week44-bs015.html#___sec14" style="font-size: 80%;">Visualizing the Tree, Classification</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs016.html#___sec15" style="font-size: 80%;">Visualizing the Tree, The Moons</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs017.html#___sec16" style="font-size: 80%;">Algorithms for Setting up Decision Trees</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs018.html#___sec17" style="font-size: 80%;">The CART algorithm for Classification</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs019.html#___sec18" style="font-size: 80%;">The CART algorithm for Regression</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs020.html#___sec19" style="font-size: 80%;">Computing the Gini index</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week44-bs022.html#___sec21" style="font-size: 80%;">Computing the Gini Factor</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs023.html#___sec22" style="font-size: 80%;">Entropy and the ID3 algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs024.html#___sec23" style="font-size: 80%;">Implementing the ID3 Algorithm</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week44-bs026.html#___sec25" style="font-size: 80%;">Another example, the moons again</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs027.html#___sec26" style="font-size: 80%;">Playing around with regions</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs028.html#___sec27" style="font-size: 80%;">Regression trees</a></li>
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<!-- navigation toc: --> <li><a href="#___sec28" style="font-size: 80%;">Final regressor code</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs030.html#___sec29" style="font-size: 80%;">Pros and cons of trees, pros</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs031.html#___sec30" style="font-size: 80%;">Disadvantages</a></li>
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<!-- 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>
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<!-- navigation toc: --> <li><a href="._week44-bs033.html#___sec32" style="font-size: 80%;">An Overview of Ensemble Methods</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs034.html#___sec33" style="font-size: 80%;">Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs035.html#___sec34" style="font-size: 80%;">More bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs036.html#___sec35" style="font-size: 80%;">Simple Voting Example, head or tail</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs037.html#___sec36" style="font-size: 80%;">Using the Voting Classifier</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs038.html#___sec37" style="font-size: 80%;">Please, not the moons again! Voting and Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week44-bs039.html#___sec38" style="font-size: 80%;">Bagging Examples</a></li>
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<!-- 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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</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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<div class="container">
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<a name="part0029"></a>
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<!-- !split -->
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<h2 id="___sec28" class="anchor">Final regressor code </h2>
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "default" -->
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<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.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeRegressor
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tree_reg1 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>, max_depth<span style="color: #666666">=2</span>)
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tree_reg2 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>, max_depth<span style="color: #666666">=3</span>)
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tree_reg1<span style="color: #666666">.</span>fit(X, y)
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tree_reg2<span style="color: #666666">.</span>fit(X, y)
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<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plot_regression_predictions</span>(tree_reg, X, y, axes<span style="color: #666666">=</span>[<span style="color: #666666">0</span>, <span style="color: #666666">1</span>, <span style="color: #666666">-0.2</span>, <span style="color: #666666">1</span>], ylabel<span style="color: #666666">=</span><span style="color: #BA2121">"$y$"</span>):
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x1 <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">500</span>)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
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y_pred <span style="color: #666666">=</span> tree_reg<span style="color: #666666">.</span>predict(x1)
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plt<span style="color: #666666">.</span>axis(axes)
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plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">"$x_1$"</span>, fontsize<span style="color: #666666">=18</span>)
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<span style="color: #008000; font-weight: bold">if</span> ylabel:
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plt<span style="color: #666666">.</span>ylabel(ylabel, fontsize<span style="color: #666666">=18</span>, rotation<span style="color: #666666">=0</span>)
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plt<span style="color: #666666">.</span>plot(X, y, <span style="color: #BA2121">"b."</span>)
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plt<span style="color: #666666">.</span>plot(x1, y_pred, <span style="color: #BA2121">"r.-"</span>, linewidth<span style="color: #666666">=2</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">r"$\hat{y}$"</span>)
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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>))
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plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">121</span>)
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plot_regression_predictions(tree_reg1, X, y)
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<span style="color: #008000; font-weight: bold">for</span> split, style <span style="color: #AA22FF; font-weight: bold">in</span> ((<span style="color: #666666">0.1973</span>, <span style="color: #BA2121">"k-"</span>), (<span style="color: #666666">0.0917</span>, <span style="color: #BA2121">"k--"</span>), (<span style="color: #666666">0.7718</span>, <span style="color: #BA2121">"k--"</span>)):
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plt<span style="color: #666666">.</span>plot([split, split], [<span style="color: #666666">-0.2</span>, <span style="color: #666666">1</span>], style, linewidth<span style="color: #666666">=2</span>)
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plt<span style="color: #666666">.</span>text(<span style="color: #666666">0.21</span>, <span style="color: #666666">0.65</span>, <span style="color: #BA2121">"Depth=0"</span>, fontsize<span style="color: #666666">=15</span>)
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plt<span style="color: #666666">.</span>text(<span style="color: #666666">0.01</span>, <span style="color: #666666">0.2</span>, <span style="color: #BA2121">"Depth=1"</span>, fontsize<span style="color: #666666">=13</span>)
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plt<span style="color: #666666">.</span>text(<span style="color: #666666">0.65</span>, <span style="color: #666666">0.8</span>, <span style="color: #BA2121">"Depth=1"</span>, fontsize<span style="color: #666666">=13</span>)
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plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">"upper center"</span>, fontsize<span style="color: #666666">=18</span>)
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plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">"max_depth=2"</span>, fontsize<span style="color: #666666">=14</span>)
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plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">122</span>)
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plot_regression_predictions(tree_reg2, X, y, ylabel<span style="color: #666666">=</span><span style="color: #008000">None</span>)
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<span style="color: #008000; font-weight: bold">for</span> split, style <span style="color: #AA22FF; font-weight: bold">in</span> ((<span style="color: #666666">0.1973</span>, <span style="color: #BA2121">"k-"</span>), (<span style="color: #666666">0.0917</span>, <span style="color: #BA2121">"k--"</span>), (<span style="color: #666666">0.7718</span>, <span style="color: #BA2121">"k--"</span>)):
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plt<span style="color: #666666">.</span>plot([split, split], [<span style="color: #666666">-0.2</span>, <span style="color: #666666">1</span>], style, linewidth<span style="color: #666666">=2</span>)
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<span style="color: #008000; font-weight: bold">for</span> split <span style="color: #AA22FF; font-weight: bold">in</span> (<span style="color: #666666">0.0458</span>, <span style="color: #666666">0.1298</span>, <span style="color: #666666">0.2873</span>, <span style="color: #666666">0.9040</span>):
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plt<span style="color: #666666">.</span>plot([split, split], [<span style="color: #666666">-0.2</span>, <span style="color: #666666">1</span>], <span style="color: #BA2121">"k:"</span>, linewidth<span style="color: #666666">=1</span>)
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plt<span style="color: #666666">.</span>text(<span style="color: #666666">0.3</span>, <span style="color: #666666">0.5</span>, <span style="color: #BA2121">"Depth=2"</span>, fontsize<span style="color: #666666">=13</span>)
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plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">"max_depth=3"</span>, fontsize<span style="color: #666666">=14</span>)
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plt<span style="color: #666666">.</span>show()
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</pre></div>
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "default" -->
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<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>tree_reg1 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>)
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tree_reg2 <span style="color: #666666">=</span> DecisionTreeRegressor(random_state<span style="color: #666666">=42</span>, min_samples_leaf<span style="color: #666666">=10</span>)
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tree_reg1<span style="color: #666666">.</span>fit(X, y)
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tree_reg2<span style="color: #666666">.</span>fit(X, y)
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x1 <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linspace(<span style="color: #666666">0</span>, <span style="color: #666666">1</span>, <span style="color: #666666">500</span>)<span style="color: #666666">.</span>reshape(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
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y_pred1 <span style="color: #666666">=</span> tree_reg1<span style="color: #666666">.</span>predict(x1)
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y_pred2 <span style="color: #666666">=</span> tree_reg2<span style="color: #666666">.</span>predict(x1)
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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>))
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plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">121</span>)
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plt<span style="color: #666666">.</span>plot(X, y, <span style="color: #BA2121">"b."</span>)
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plt<span style="color: #666666">.</span>plot(x1, y_pred1, <span style="color: #BA2121">"r.-"</span>, linewidth<span style="color: #666666">=2</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">r"$\hat{y}$"</span>)
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|
plt<span style="color: #666666">.</span>axis([<span style="color: #666666">0</span>, <span style="color: #666666">1</span>, <span style="color: #666666">-0.2</span>, <span style="color: #666666">1.1</span>])
|
|
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">"$x_1$"</span>, fontsize<span style="color: #666666">=18</span>)
|
|
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">"$y$"</span>, fontsize<span style="color: #666666">=18</span>, rotation<span style="color: #666666">=0</span>)
|
|
plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">"upper center"</span>, fontsize<span style="color: #666666">=18</span>)
|
|
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">"No restrictions"</span>, fontsize<span style="color: #666666">=14</span>)
|
|
|
|
plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">122</span>)
|
|
plt<span style="color: #666666">.</span>plot(X, y, <span style="color: #BA2121">"b."</span>)
|
|
plt<span style="color: #666666">.</span>plot(x1, y_pred2, <span style="color: #BA2121">"r.-"</span>, linewidth<span style="color: #666666">=2</span>, label<span style="color: #666666">=</span><span style="color: #BA2121">r"$\hat{y}$"</span>)
|
|
plt<span style="color: #666666">.</span>axis([<span style="color: #666666">0</span>, <span style="color: #666666">1</span>, <span style="color: #666666">-0.2</span>, <span style="color: #666666">1.1</span>])
|
|
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">"$x_1$"</span>, fontsize<span style="color: #666666">=18</span>)
|
|
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">"min_samples_leaf={}"</span><span style="color: #666666">.</span>format(tree_reg2<span style="color: #666666">.</span>min_samples_leaf), fontsize<span style="color: #666666">=14</span>)
|
|
|
|
plt<span style="color: #666666">.</span>show()
|
|
</pre></div>
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<p>
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