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<section id="adaboostclassifier">
<h1>AdaBoostClassifier<a class="headerlink" href="#adaboostclassifier" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">AdaBoostClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier" title="Link to this definition">#</a></dt>
<dd><p>An AdaBoost classifier.</p>
<p>An AdaBoost <a class="reference internal" href="#r33e4ec8c4ad5-1" id="id1">[1]</a> classifier is a meta-estimator that begins by fitting a
classifier on the original dataset and then fits additional copies of the
classifier on the same dataset but where the weights of incorrectly
classified instances are adjusted such that subsequent classifiers focus
more on difficult cases.</p>
<p>This class implements the algorithm based on <a class="reference internal" href="#r33e4ec8c4ad5-2" id="id2">[2]</a>.</p>
<p>Read more in the <a class="reference internal" href="../ensemble.html#adaboost"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.14.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>estimator</strong><span class="classifier">object, default=None</span></dt><dd><p>The base estimator from which the boosted ensemble is built.
Support for sample weighting is required, as well as proper
<code class="docutils literal notranslate"><span class="pre">classes_</span></code> and <code class="docutils literal notranslate"><span class="pre">n_classes_</span></code> attributes. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then
the base estimator is <a class="reference internal" href="sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">DecisionTreeClassifier</span></code></a>
initialized with <code class="docutils literal notranslate"><span class="pre">max_depth=1</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.2: </span><code class="docutils literal notranslate"><span class="pre">base_estimator</span></code> was renamed to <code class="docutils literal notranslate"><span class="pre">estimator</span></code>.</p>
</div>
</dd>
<dt><strong>n_estimators</strong><span class="classifier">int, default=50</span></dt><dd><p>The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
Values must be in the range <code class="docutils literal notranslate"><span class="pre">[1,</span> <span class="pre">inf)</span></code>.</p>
</dd>
<dt><strong>learning_rate</strong><span class="classifier">float, default=1.0</span></dt><dd><p>Weight applied to each classifier at each boosting iteration. A higher
learning rate increases the contribution of each classifier. There is
a trade-off between the <code class="docutils literal notranslate"><span class="pre">learning_rate</span></code> and <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> parameters.
Values must be in the range <code class="docutils literal notranslate"><span class="pre">(0.0,</span> <span class="pre">inf)</span></code>.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘SAMME’}, default=’SAMME’</span></dt><dd><p>Use the SAMME discrete boosting algorithm.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 1.6: </span><code class="docutils literal notranslate"><span class="pre">algorithm</span></code> is deprecated and will be removed in version 1.8. This
estimator only implements the ‘SAMME’ algorithm.</p>
</div>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the random seed given at each <code class="docutils literal notranslate"><span class="pre">estimator</span></code> at each
boosting iteration.
Thus, it is only used when <code class="docutils literal notranslate"><span class="pre">estimator</span></code> exposes a <code class="docutils literal notranslate"><span class="pre">random_state</span></code>.
Pass an int for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>estimator_</strong><span class="classifier">estimator</span></dt><dd><p>The base estimator from which the ensemble is grown.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.2: </span><code class="docutils literal notranslate"><span class="pre">base_estimator_</span></code> was renamed to <code class="docutils literal notranslate"><span class="pre">estimator_</span></code>.</p>
</div>
</dd>
<dt><strong>estimators_</strong><span class="classifier">list of classifiers</span></dt><dd><p>The collection of fitted sub-estimators.</p>
</dd>
<dt><strong>classes_</strong><span class="classifier">ndarray of shape (n_classes,)</span></dt><dd><p>The classes labels.</p>
</dd>
<dt><strong>n_classes_</strong><span class="classifier">int</span></dt><dd><p>The number of classes.</p>
</dd>
<dt><strong>estimator_weights_</strong><span class="classifier">ndarray of floats</span></dt><dd><p>Weights for each estimator in the boosted ensemble.</p>
</dd>
<dt><strong>estimator_errors_</strong><span class="classifier">ndarray of floats</span></dt><dd><p>Classification error for each estimator in the boosted
ensemble.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.ensemble.AdaBoostClassifier.feature_importances_" title="sklearn.ensemble.AdaBoostClassifier.feature_importances_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_importances_</span></code></a><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>The impurity-based feature importances.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AdaBoostRegressor</span></code></a></dt><dd><p>An AdaBoost regressor that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction.</p>
</dd>
<dt><a class="reference internal" href="sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GradientBoostingClassifier</span></code></a></dt><dd><p>GB builds an additive model in a forward stage-wise fashion. Regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.</p>
</dd>
<dt><a class="reference internal" href="sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.tree.DecisionTreeClassifier</span></code></a></dt><dd><p>A non-parametric supervised learning method used for classification. Creates a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r33e4ec8c4ad5-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p>Y. Freund, R. Schapire, “A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting”, 1995.</p>
</div>
<div class="citation" id="r33e4ec8c4ad5-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://doi.org/10.4310/SII.2009.v2.n3.a8">J. Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class adaboost.”
Statistics and its Interface 2.3 (2009): 349-360.</a></p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">AdaBoostClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_redundant</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </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="go">AdaBoostClassifier(n_estimators=100, random_state=0)</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="go">array([1])</span>
<span class="gp">>>> </span><span class="n">clf</span><span class="o">.</span><span class="n">score</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="go">0.96...</span>
</pre></div>
</div>
<p>For a detailed example of using AdaBoost to fit a sequence of DecisionTrees
as weaklearners, please refer to
<a class="reference internal" href="../../auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a>.</p>
<p>For a detailed example of using AdaBoost to fit a non-linearly seperable
classification dataset composed of two Gaussian quantiles clusters, please
refer to <a class="reference internal" href="../../auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py"><span class="std std-ref">Two-class AdaBoost</span></a>.</p>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.decision_function">
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.decision_function" title="Link to this definition">#</a></dt>
<dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">ndarray of shape of (n_samples, k)</span></dt><dd><p>The decision function of the input samples. The order of
outputs is the same as that of the <a class="reference internal" href="../../glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a> attribute.
Binary classification is a special cases with <code class="docutils literal notranslate"><span class="pre">k</span> <span class="pre">==</span> <span class="pre">1</span></code>,
otherwise <code class="docutils literal notranslate"><span class="pre">k==n_classes</span></code>. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in <code class="docutils literal notranslate"><span class="pre">classes_</span></code>, respectively.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.feature_importances_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">feature_importances_</span></span><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.feature_importances_" title="Link to this definition">#</a></dt>
<dd><p>The impurity-based feature importances.</p>
<p>The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.</p>
<p>Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
<a class="reference internal" href="sklearn.inspection.permutation_importance.html#sklearn.inspection.permutation_importance" title="sklearn.inspection.permutation_importance"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.inspection.permutation_importance</span></code></a> as an alternative.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>feature_importances_</strong><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>The feature importances.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.fit" title="Link to this definition">#</a></dt>
<dd><p>Build a boosted classifier/regressor from the training set (X, y).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>The target values.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, the sample weights are initialized to
1 / n_samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Raise <code class="docutils literal notranslate"><span class="pre">NotImplementedError</span></code>.</p>
<p>This estimator does not support metadata routing yet.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict classes for X.</p>
<p>The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The predicted classes.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.predict_log_proba">
<span class="sig-name descname"><span class="pre">predict_log_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L840"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict_log_proba" title="Link to this definition">#</a></dt>
<dd><p>Predict class log-probabilities for X.</p>
<p>The predicted class log-probabilities of an input sample is computed as
the weighted mean predicted class log-probabilities of the classifiers
in the ensemble.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>p</strong><span class="classifier">ndarray of shape (n_samples, n_classes)</span></dt><dd><p>The class probabilities of the input samples. The order of
outputs is the same of that of the <a class="reference internal" href="../../glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a> attribute.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L782"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.predict_proba" title="Link to this definition">#</a></dt>
<dd><p>Predict class probabilities for X.</p>
<p>The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>p</strong><span class="classifier">ndarray of shape (n_samples, n_classes)</span></dt><dd><p>The class probabilities of the input samples. The order of
outputs is the same of that of the <a class="reference internal" href="../../glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a> attribute.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L465"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.score" title="Link to this definition">#</a></dt>
<dd><p>Return the mean accuracy on the given test data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.set_fit_request">
<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble._weight_boosting.AdaBoostClassifier"><span class="pre">AdaBoostClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_fit_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_params" title="Link to this definition">#</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.set_score_request">
<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble._weight_boosting.AdaBoostClassifier"><span class="pre">AdaBoostClassifier</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.set_score_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_decision_function">
<span class="sig-name descname"><span class="pre">staged_decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L712"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_decision_function" title="Link to this definition">#</a></dt>
<dd><p>Compute decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code> for each boosting iteration.</p>
<p>This method allows monitoring (i.e. determine error on testing set)
after each boosting iteration.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">generator of ndarray of shape (n_samples, k)</span></dt><dd><p>The decision function of the input samples. The order of
outputs is the same of that of the <a class="reference internal" href="../../glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a> attribute.
Binary classification is a special cases with <code class="docutils literal notranslate"><span class="pre">k</span> <span class="pre">==</span> <span class="pre">1</span></code>,
otherwise <code class="docutils literal notranslate"><span class="pre">k==n_classes</span></code>. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in <code class="docutils literal notranslate"><span class="pre">classes_</span></code>, respectively.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_predict">
<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict" title="Link to this definition">#</a></dt>
<dd><p>Return staged predictions for X.</p>
<p>The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.</p>
<p>This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">generator of ndarray of shape (n_samples,)</span></dt><dd><p>The predicted classes.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_predict_proba">
<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict_proba" title="Link to this definition">#</a></dt>
<dd><p>Predict class probabilities for X.</p>
<p>The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.</p>
<p>This generator method yields the ensemble predicted class probabilities
after each iteration of boosting and therefore allows monitoring, such
as to determine the predicted class probabilities on a test set after
each boost.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>p</strong><span class="classifier">generator of ndarray of shape (n_samples,)</span></dt><dd><p>The class probabilities of the input samples. The order of
outputs is the same of that of the <a class="reference internal" href="../../glossary.html#term-classes_"><span class="xref std std-term">classes_</span></a> attribute.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.AdaBoostClassifier.staged_score">
<span class="sig-name descname"><span class="pre">staged_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_weight_boosting.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.AdaBoostClassifier.staged_score" title="Link to this definition">#</a></dt>
<dd><p>Return staged scores for X, y.</p>
<p>This generator method yields the ensemble score after each iteration of
boosting and therefore allows monitoring, such as to determine the
score on a test set after each boost.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Labels for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>z</strong><span class="classifier">float</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A comparison of several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets."><img alt="" src="../../_images/sphx_glr_plot_classifier_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</span></a></p>
<div class="sphx-glr-thumbnail-title">Classifier comparison</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al [1]_."><img alt="" src="../../_images/sphx_glr_plot_adaboost_multiclass_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Multi-class AdaBoosted Decision Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset."><img alt="" src="../../_images/sphx_glr_plot_forest_iris_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/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="This example fits an AdaBoosted decision stump on a non-linearly separable classification dataset composed of two "Gaussian quantiles" clusters (see sklearn.datasets.make_gaussian_quantiles) and plots the decision boundary and decision scores. The distributions of decision scores are shown separately for samples of class A and B. The predicted class label for each sample is determined by the sign of the decision score. Samples with decision scores greater than zero are classified as B, and are otherwise classified as A. The magnitude of a decision score determines the degree of likeness with the predicted class label. Additionally, a new dataset could be constructed containing a desired purity of class B, for example, by only selecting samples with a decision score above some value."><img alt="" src="../../_images/sphx_glr_plot_adaboost_twoclass_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/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>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al [1]_."><img alt="" src="../../_images/sphx_glr_plot_adaboost_multiclass_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py"><span class="std std-ref">Multi-class AdaBoosted Decision Trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Multi-class AdaBoosted Decision Trees</div>
</div></div></section>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier"><code class="docutils literal notranslate"><span class="pre">AdaBoostClassifier</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.decision_function"><code class="docutils literal notranslate"><span class="pre">decision_function</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.feature_importances_"><code class="docutils literal notranslate"><span class="pre">feature_importances_</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.predict_log_proba"><code class="docutils literal notranslate"><span class="pre">predict_log_proba</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">predict_proba</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.set_score_request"><code class="docutils literal notranslate"><span class="pre">set_score_request</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.staged_decision_function"><code class="docutils literal notranslate"><span class="pre">staged_decision_function</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict"><code class="docutils literal notranslate"><span class="pre">staged_predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.staged_predict_proba"><code class="docutils literal notranslate"><span class="pre">staged_predict_proba</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.AdaBoostClassifier.staged_score"><code class="docutils literal notranslate"><span class="pre">staged_score</span></code></a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#gallery-examples">Gallery examples</a></li>
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