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<li><a class="reference internal" href="#">Decision Tree Regression with AdaBoost</a></li>
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<div class="section" id="decision-tree-regression-with-adaboost">
<span id="example-ensemble-plot-adaboost-regression-py"></span><h1>Decision Tree Regression with AdaBoost<a class="headerlink" href="#decision-tree-regression-with-adaboost" title="Permalink to this headline">¶</a></h1>
<p>A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tree
regressor. As the number of boosts is increased the regressor can fit more
detail.</p>
<table class="docutils footnote" frame="void" id="id1" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label">[1]</td><td><ol class="first last upperalpha simple" start="8">
<li>Drucker, “Improving Regressors using Boosting Techniques”, 1997.</li>
</ol>
</td></tr>
</tbody>
</table>
<img alt="../../_images/plot_adaboost_regression_001.png" class="align-center" src="../../_images/plot_adaboost_regression_001.png" />
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/plot_adaboost_regression.py"><tt class="xref download docutils literal"><span class="pre">plot_adaboost_regression.py</span></tt></a></p>
<div class="highlight-python"><div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="n">__doc__</span><span class="p">)</span>
<span class="c"># Author: Noel Dawe <[email protected]></span>
<span class="c">#</span>
<span class="c"># License: BSD 3 clause</span>
<span class="c"># importing necessary libraries</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor"><span class="n">DecisionTreeRegressor</span></a>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor"><span class="n">AdaBoostRegressor</span></a>
<span class="c"># Create the dataset</span>
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">100</span><span class="p">)[:,</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/arrays.indexing.html#numpy.newaxis"><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span></a><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.sin.html#numpy.sin"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> <span class="o">+</span> <a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.sin.html#numpy.sin"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="mi">6</span> <span class="o">*</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> <span class="o">+</span> <span class="n">rng</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="c"># Fit regression model</span>
<span class="n">clf_1</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor"><span class="n">DecisionTreeRegressor</span></a><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">clf_2</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor"><span class="n">AdaBoostRegressor</span></a><span class="p">(</span><a href="../../modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor"><span class="n">DecisionTreeRegressor</span></a><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="n">clf_1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">clf_2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="c"># Predict</span>
<span class="n">y_1</span> <span class="o">=</span> <span class="n">clf_1</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">y_2</span> <span class="o">=</span> <span class="n">clf_2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="c"># Plot the results</span>
<a href="http://matplotlib.org/api/figure_api.html#matplotlib.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter"><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s">"k"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"training samples"</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y_1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s">"g"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"n_estimators=1"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y_2</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s">"r"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">"n_estimators=300"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xlabel"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s">"data"</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.ylabel"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s">"target"</span><span class="p">)</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s">"Boosted Decision Tree Regression"</span><span class="p">)</span>
<a href="http://matplotlib.org/api/legend_api.html#matplotlib.legend"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
<a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<p><strong>Total running time of the example:</strong> 0.44 seconds
( 0 minutes 0.44 seconds)</p>
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