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<li><a class="reference internal" href="#">OOB Errors for Random Forests</a></li>
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<section class="sphx-glr-example-title" id="oob-errors-for-random-forests">
<span id="sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py"></span><h1>OOB Errors for Random Forests<a class="headerlink" href="#oob-errors-for-random-forests" title="Link to this heading">¶</a></h1>
<p>The <code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> is trained using <em>bootstrap aggregation</em>, where
each new tree is fit from a bootstrap sample of the training observations
<span class="math notranslate nohighlight">\(z_i = (x_i, y_i)\)</span>. The <em>out-of-bag</em> (OOB) error is the average error for
each <span class="math notranslate nohighlight">\(z_i\)</span> calculated using predictions from the trees that do not
contain <span class="math notranslate nohighlight">\(z_i\)</span> in their respective bootstrap sample. This allows the
<code class="docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code> to be fit and validated whilst being trained <a class="footnote-reference brackets" href="#id2" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
<p>The example below demonstrates how the OOB error can be measured at the
addition of each new tree during training. The resulting plot allows a
practitioner to approximate a suitable value of <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> at which the
error stabilizes.</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>T. Hastie, R. Tibshirani and J. Friedman, “Elements of Statistical
Learning Ed. 2”, p592-593, Springer, 2009.</p>
</aside>
</aside>
<img src="../../_images/sphx_glr_plot_ensemble_oob_001.png" srcset="../../_images/sphx_glr_plot_ensemble_oob_001.png" alt="plot ensemble oob" class = "sphx-glr-single-img"/><div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Kian Ho <[email protected]></span>
<span class="c1"># Gilles Louppe <[email protected]></span>
<span class="c1"># Andreas Mueller <[email protected]></span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 Clause</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/collections.html#collections.OrderedDict" title="collections.OrderedDict" class="sphx-glr-backref-module-collections sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OrderedDict</span></a>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a>
<span class="n">RANDOM_STATE</span> <span class="o">=</span> <span class="mi">123</span>
<span class="c1"># Generate a binary classification dataset.</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a><span class="p">(</span>
<span class="n">n_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
<span class="n">n_features</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
<span class="n">n_clusters_per_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">n_informative</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
<span class="p">)</span>
<span class="c1"># NOTE: Setting the `warm_start` construction parameter to `True` disables</span>
<span class="c1"># support for parallelized ensembles but is necessary for tracking the OOB</span>
<span class="c1"># error trajectory during training.</span>
<span class="n">ensemble_clfs</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span>
<span class="s2">"RandomForestClassifier, max_features='sqrt'"</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">(</span>
<span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">max_features</span><span class="o">=</span><span class="s2">"sqrt"</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="p">(</span>
<span class="s2">"RandomForestClassifier, max_features='log2'"</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">(</span>
<span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">max_features</span><span class="o">=</span><span class="s2">"log2"</span><span class="p">,</span>
<span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="p">(</span>
<span class="s2">"RandomForestClassifier, max_features=None"</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">(</span>
<span class="n">warm_start</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">max_features</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="p">]</span>
<span class="c1"># Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.</span>
<span class="n">error_rate</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.html#collections.OrderedDict" title="collections.OrderedDict" class="sphx-glr-backref-module-collections sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OrderedDict</span></a><span class="p">((</span><span class="n">label</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">ensemble_clfs</span><span class="p">)</span>
<span class="c1"># Range of `n_estimators` values to explore.</span>
<span class="n">min_estimators</span> <span class="o">=</span> <span class="mi">15</span>
<span class="n">max_estimators</span> <span class="o">=</span> <span class="mi">150</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">ensemble_clfs</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">min_estimators</span><span class="p">,</span> <span class="n">max_estimators</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
<span class="n">clf</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
<span class="n">clf</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="c1"># Record the OOB error for each `n_estimators=i` setting.</span>
<span class="n">oob_error</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">clf</span><span class="o">.</span><span class="n">oob_score_</span>
<span class="n">error_rate</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">oob_error</span><span class="p">))</span>
<span class="c1"># Generate the "OOB error rate" vs. "n_estimators" plot.</span>
<span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">clf_err</span> <span class="ow">in</span> <span class="n">error_rate</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">clf_err</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">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="matplotlib.pyplot.xlim" 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">xlim</span></a><span class="p">(</span><span class="n">min_estimators</span><span class="p">,</span> <span class="n">max_estimators</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">"n_estimators"</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">"OOB error rate"</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><span class="n">loc</span><span class="o">=</span><span class="s2">"upper right"</span><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>
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