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<li><a class="reference internal" href="#">Decision Tree Regression with AdaBoost</a><ul>
<li><a class="reference internal" href="#preparing-the-data">Preparing the data</a></li>
<li><a class="reference internal" href="#training-and-prediction-with-decisiontree-and-adaboost-regressors">Training and prediction with DecisionTree and AdaBoost Regressors</a></li>
<li><a class="reference internal" href="#plotting-the-results">Plotting the results</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-ensemble-plot-adaboost-regression-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code or to run this example in your browser via JupyterLite or Binder</p>
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<section class="sphx-glr-example-title" id="decision-tree-regression-with-adaboost">
<span id="sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py"></span><h1>Decision Tree Regression with AdaBoost<a class="headerlink" href="#decision-tree-regression-with-adaboost" title="Link to this heading">¶</a></h1>
<p>A decision tree is boosted using the AdaBoost.R2 <a class="footnote-reference brackets" href="#id2" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> 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>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id2" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3">H. Drucker, “Improving Regressors using Boosting Techniques”, 1997.</a></p>
</aside>
</aside>
<section id="preparing-the-data">
<h2>Preparing the data<a class="headerlink" href="#preparing-the-data" title="Link to this heading">¶</a></h2>
<p>First, we prepare dummy data with a sinusoidal relationship and some gaussian noise.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Noel Dawe <[email protected]></span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 clause</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><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="https://numpy.org/doc/stable/reference/constants.html#numpy.newaxis" title="numpy.newaxis" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><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="https://numpy.org/doc/stable/reference/generated/numpy.sin.html#numpy.sin" title="numpy.sin" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><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="https://numpy.org/doc/stable/reference/generated/numpy.sin.html#numpy.sin" title="numpy.sin" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-data"><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>
</pre></div>
</div>
</section>
<section id="training-and-prediction-with-decisiontree-and-adaboost-regressors">
<h2>Training and prediction with DecisionTree and AdaBoost Regressors<a class="headerlink" href="#training-and-prediction-with-decisiontree-and-adaboost-regressors" title="Link to this heading">¶</a></h2>
<p>Now, we define the classifiers and fit them to the data.
Then we predict on that same data to see how well they could fit it.
The first regressor is a <code class="docutils literal notranslate"><span class="pre">DecisionTreeRegressor</span></code> with <code class="docutils literal notranslate"><span class="pre">max_depth=4</span></code>.
The second regressor is an <code class="docutils literal notranslate"><span class="pre">AdaBoostRegressor</span></code> with a <code class="docutils literal notranslate"><span class="pre">DecisionTreeRegressor</span></code>
of <code class="docutils literal notranslate"><span class="pre">max_depth=4</span></code> as base learner and will be built with <code class="docutils literal notranslate"><span class="pre">n_estimators=300</span></code>
of those base learners.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><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" title="sklearn.ensemble.AdaBoostRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AdaBoostRegressor</span></a>
<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" title="sklearn.tree.DecisionTreeRegressor" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DecisionTreeRegressor</span></a>
<span class="n">regr_1</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><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">regr_2</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">AdaBoostRegressor</span></a><span class="p">(</span>
<a href="../../modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor" title="sklearn.tree.DecisionTreeRegressor" class="sphx-glr-backref-module-sklearn-tree sphx-glr-backref-type-py-class sphx-glr-backref-instance"><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">regr_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">regr_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="n">y_1</span> <span class="o">=</span> <span class="n">regr_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">regr_2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="plotting-the-results">
<h2>Plotting the results<a class="headerlink" href="#plotting-the-results" title="Link to this heading">¶</a></h2>
<p>Finally, we plot how well our two regressors,
single decision tree regressor and AdaBoost regressor, could fit the data.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">colors</span> <span class="o">=</span> <a href="https://seaborn.pydata.org/generated/seaborn.color_palette.html#seaborn.color_palette" title="seaborn.color_palette" class="sphx-glr-backref-module-seaborn sphx-glr-backref-type-py-function"><span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span></a><span class="p">(</span><span class="s2">"colorblind"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">()</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter" title="matplotlib.pyplot.scatter" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"training samples"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><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">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"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="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">"data"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">"target"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s2">"Boosted Decision Tree Regression"</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_adaboost_regression_001.png" srcset="../../_images/sphx_glr_plot_adaboost_regression_001.png" alt="Boosted Decision Tree Regression" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.450 seconds)</p>
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<p class="rubric">Related examples</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A 1D regression with decision tree."><img alt="" src="../../_images/sphx_glr_plot_tree_regression_thumb.png" />
<p><a class="reference internal" href="../tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py"><span class="std std-ref">Decision Tree Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Decision Tree Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example to illustrate multi-output regression with decision tree."><img alt="" src="../../_images/sphx_glr_plot_tree_regression_multioutput_thumb.png" />
<p><a class="reference internal" href="../tree/plot_tree_regression_multioutput.html#sphx-glr-auto-examples-tree-plot-tree-regression-multioutput-py"><span class="std std-ref">Multi-output Decision Tree Regression</span></a></p>
<div class="sphx-glr-thumbnail-title">Multi-output Decision Tree Regression</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the i..."><img alt="" src="../../_images/sphx_glr_plot_forest_iris_thumb.png" />
<p><a class="reference internal" href="plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py"><span class="std std-ref">Plot the decision surfaces of ensembles of trees on the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot the decision surfaces of ensembles of trees on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surface of a decision tree trained on pairs of features of the iris dataset."><img alt="" src="../../_images/sphx_glr_plot_iris_dtc_thumb.png" />
<p><a class="reference internal" href="../tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py"><span class="std std-ref">Plot the decision surface of decision trees trained on the iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Plot the decision surface of decision trees trained on the iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example fits an AdaBoosted decision stump on a non-linearly separable classification datas..."><img alt="" src="../../_images/sphx_glr_plot_adaboost_twoclass_thumb.png" />
<p><a class="reference internal" href="plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a></p>
<div class="sphx-glr-thumbnail-title">Two-class AdaBoost</div>
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