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<div class="section" id="sklearn-ensemble-gradientboostingclassifier">
<h1>3.2.3.3.5. sklearn.ensemble.GradientBoostingClassifier<a class="headerlink" href="#sklearn-ensemble-gradientboostingclassifier" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.ensemble.GradientBoostingClassifier">
<em class="property">class </em><tt class="descclassname">sklearn.ensemble.</tt><tt class="descname">GradientBoostingClassifier</tt><big>(</big><em>loss='deviance'</em>, <em>learning_rate=0.1</em>, <em>n_estimators=100</em>, <em>subsample=1.0</em>, <em>min_samples_split=2</em>, <em>min_samples_leaf=1</em>, <em>max_depth=3</em>, <em>init=None</em>, <em>random_state=None</em>, <em>max_features=None</em>, <em>verbose=0</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier" title="Permalink to this definition">¶</a></dt>
<dd><p>Gradient Boosting for classification.</p>
<p>GB builds an additive model in a
forward stage-wise fashion; it allows for the optimization of
arbitrary differentiable loss functions. In each stage <tt class="docutils literal"><span class="pre">n_classes_</span></tt>
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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>loss</strong> : {‘deviance’}, optional (default=’deviance’)</p>
<blockquote>
<div><p>loss function to be optimized. ‘deviance’ refers to
deviance (= logistic regression) for classification
with probabilistic outputs.</p>
</div></blockquote>
<p><strong>learning_rate</strong> : float, optional (default=0.1)</p>
<blockquote>
<div><p>learning rate shrinks the contribution of each tree by <cite>learning_rate</cite>.
There is a trade-off between learning_rate and n_estimators.</p>
</div></blockquote>
<p><strong>n_estimators</strong> : int (default=100)</p>
<blockquote>
<div><p>The number of boosting stages to perform. Gradient boosting
is fairly robust to over-fitting so a large number usually
results in better performance.</p>
</div></blockquote>
<p><strong>max_depth</strong> : integer, optional (default=3)</p>
<blockquote>
<div><p>maximum depth of the individual regression estimators. The maximum
depth limits the number of nodes in the tree. Tune this parameter
for best performance; the best value depends on the interaction
of the input variables.</p>
</div></blockquote>
<p><strong>min_samples_split</strong> : integer, optional (default=2)</p>
<blockquote>
<div><p>The minimum number of samples required to split an internal node.</p>
</div></blockquote>
<p><strong>min_samples_leaf</strong> : integer, optional (default=1)</p>
<blockquote>
<div><p>The minimum number of samples required to be at a leaf node.</p>
</div></blockquote>
<p><strong>subsample</strong> : float, optional (default=1.0)</p>
<blockquote>
<div><p>The fraction of samples to be used for fitting the individual base
learners. If smaller than 1.0 this results in Stochastic Gradient
Boosting. <cite>subsample</cite> interacts with the parameter <cite>n_estimators</cite>.
Choosing <cite>subsample < 1.0</cite> leads to a reduction of variance
and an increase in bias.</p>
</div></blockquote>
<p><strong>max_features</strong> : int, float, string or None, optional (default=”auto”)</p>
<blockquote>
<div><dl class="docutils">
<dt>The number of features to consider when looking for the best split:</dt>
<dd><ul class="first last simple">
<li>If int, then consider <cite>max_features</cite> features at each split.</li>
<li>If float, then <cite>max_features</cite> is a percentage and
<cite>int(max_features * n_features)</cite> features are considered at each
split.</li>
<li>If “auto”, then <cite>max_features=sqrt(n_features)</cite>.</li>
<li>If “sqrt”, then <cite>max_features=sqrt(n_features)</cite>.</li>
<li>If “log2”, then <cite>max_features=log2(n_features)</cite>.</li>
<li>If None, then <cite>max_features=n_features</cite>.</li>
</ul>
</dd>
</dl>
<p>Choosing <cite>max_features < n_features</cite> leads to a reduction of variance
and an increase in bias.</p>
</div></blockquote>
<p><strong>init</strong> : BaseEstimator, None, optional (default=None)</p>
<blockquote>
<div><p>An estimator object that is used to compute the initial
predictions. <tt class="docutils literal"><span class="pre">init</span></tt> has to provide <tt class="docutils literal"><span class="pre">fit</span></tt> and <tt class="docutils literal"><span class="pre">predict</span></tt>.
If None it uses <tt class="docutils literal"><span class="pre">loss.init_estimator</span></tt>.</p>
</div></blockquote>
<p><strong>verbose</strong> : int, default: 0</p>
<blockquote class="last">
<div><p>Enable verbose output. If 1 then it prints progress and performance
once in a while (the more trees the lower the frequency).
If greater than 1 then it prints progress and performance for every tree.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier" title="sklearn.tree.DecisionTreeClassifier"><tt class="xref py py-obj docutils literal"><span class="pre">sklearn.tree.DecisionTreeClassifier</span></tt></a>, <a class="reference internal" href="sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><tt class="xref py py-obj docutils literal"><span class="pre">RandomForestClassifier</span></tt></a></p>
</div>
<p class="rubric">References</p>
<p>J. Friedman, Greedy Function Approximation: A Gradient Boosting
Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.</p>
<ol class="upperalpha simple" start="10">
<li>Friedman, Stochastic Gradient Boosting, 1999</li>
</ol>
<p>T. Hastie, R. Tibshirani and J. Friedman.
Elements of Statistical Learning Ed. 2, Springer, 2009.</p>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="31%" />
<col width="20%" />
<col width="49%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><cite>feature_importances_</cite></td>
<td>array, shape = [n_features]</td>
<td>The feature importances (the higher, the more important the feature).</td>
</tr>
<tr class="row-even"><td><cite>oob_improvement_</cite></td>
<td>array, shape = [n_estimators]</td>
<td>The improvement in loss (= deviance) on the out-of-bag samples
relative to the previous iteration.
<tt class="docutils literal"><span class="pre">oob_improvement_[0]</span></tt> is the improvement in
loss of the first stage over the <tt class="docutils literal"><span class="pre">init</span></tt> estimator.</td>
</tr>
<tr class="row-odd"><td><cite>oob_score_</cite></td>
<td>array, shape = [n_estimators]</td>
<td>Score of the training dataset obtained using an out-of-bag estimate.
The i-th score <tt class="docutils literal"><span class="pre">oob_score_[i]</span></tt> is the deviance (= loss) of the
model at iteration <tt class="docutils literal"><span class="pre">i</span></tt> on the out-of-bag sample.
Deprecated: use <cite>oob_improvement_</cite> instead.</td>
</tr>
<tr class="row-even"><td><cite>train_score_</cite></td>
<td>array, shape = [n_estimators]</td>
<td>The i-th score <tt class="docutils literal"><span class="pre">train_score_[i]</span></tt> is the deviance (= loss) of the
model at iteration <tt class="docutils literal"><span class="pre">i</span></tt> on the in-bag sample.
If <tt class="docutils literal"><span class="pre">subsample</span> <span class="pre">==</span> <span class="pre">1</span></tt> this is the deviance on the training data.</td>
</tr>
<tr class="row-odd"><td><cite>loss_</cite></td>
<td>LossFunction</td>
<td>The concrete <tt class="docutils literal"><span class="pre">LossFunction</span></tt> object.</td>
</tr>
<tr class="row-even"><td><cite>init</cite></td>
<td>BaseEstimator</td>
<td>The estimator that provides the initial predictions.
Set via the <tt class="docutils literal"><span class="pre">init</span></tt> argument or <tt class="docutils literal"><span class="pre">loss.init_estimator</span></tt>.</td>
</tr>
<tr class="row-odd"><td><cite>estimators_</cite>: list of DecisionTreeRegressor</td>
<td> </td>
<td>The collection of fitted sub-estimators.</td>
</tr>
</tbody>
</table>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.decision_function" title="sklearn.ensemble.GradientBoostingClassifier.decision_function"><tt class="xref py py-obj docutils literal"><span class="pre">decision_function</span></tt></a>(X)</td>
<td>Compute the decision function of <tt class="docutils literal"><span class="pre">X</span></tt>.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.fit" title="sklearn.ensemble.GradientBoostingClassifier.fit"><tt class="xref py py-obj docutils literal"><span class="pre">fit</span></tt></a>(X, y)</td>
<td>Fit the gradient boosting model.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.get_params" title="sklearn.ensemble.GradientBoostingClassifier.get_params"><tt class="xref py py-obj docutils literal"><span class="pre">get_params</span></tt></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.predict" title="sklearn.ensemble.GradientBoostingClassifier.predict"><tt class="xref py py-obj docutils literal"><span class="pre">predict</span></tt></a>(X)</td>
<td>Predict class for X.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.predict_proba" title="sklearn.ensemble.GradientBoostingClassifier.predict_proba"><tt class="xref py py-obj docutils literal"><span class="pre">predict_proba</span></tt></a>(X)</td>
<td>Predict class probabilities for X.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.score" title="sklearn.ensemble.GradientBoostingClassifier.score"><tt class="xref py py-obj docutils literal"><span class="pre">score</span></tt></a>(X, y)</td>
<td>Returns the mean accuracy on the given test data and labels.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.set_params" title="sklearn.ensemble.GradientBoostingClassifier.set_params"><tt class="xref py py-obj docutils literal"><span class="pre">set_params</span></tt></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.staged_decision_function" title="sklearn.ensemble.GradientBoostingClassifier.staged_decision_function"><tt class="xref py py-obj docutils literal"><span class="pre">staged_decision_function</span></tt></a>(X)</td>
<td>Compute decision function of <tt class="docutils literal"><span class="pre">X</span></tt> for each iteration.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict" title="sklearn.ensemble.GradientBoostingClassifier.staged_predict"><tt class="xref py py-obj docutils literal"><span class="pre">staged_predict</span></tt></a>(X)</td>
<td>Predict class probabilities at each stage for X.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba" title="sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba"><tt class="xref py py-obj docutils literal"><span class="pre">staged_predict_proba</span></tt></a>(X)</td>
<td>Predict class probabilities at each stage for X.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.__init__">
<tt class="descname">__init__</tt><big>(</big><em>loss='deviance'</em>, <em>learning_rate=0.1</em>, <em>n_estimators=100</em>, <em>subsample=1.0</em>, <em>min_samples_split=2</em>, <em>min_samples_leaf=1</em>, <em>max_depth=3</em>, <em>init=None</em>, <em>random_state=None</em>, <em>max_features=None</em>, <em>verbose=0</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.decision_function">
<tt class="descname">decision_function</tt><big>(</big><em>X</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the decision function of <tt class="docutils literal"><span class="pre">X</span></tt>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like of shape = [n_samples, n_features]</p>
<blockquote>
<div><p>The input samples.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>score</strong> : array, shape = [n_samples, k]</p>
<blockquote class="last">
<div><p>The decision function of the input samples. Classes are
ordered by arithmetical order. Regression and binary
classification are special cases with <tt class="docutils literal"><span class="pre">k</span> <span class="pre">==</span> <span class="pre">1</span></tt>,
otherwise <tt class="docutils literal"><span class="pre">k==n_classes</span></tt>.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="attribute">
<dt id="sklearn.ensemble.GradientBoostingClassifier.feature_importances_">
<tt class="descname">feature_importances_</tt><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.feature_importances_" title="Permalink to this definition">¶</a></dt>
<dd><dl class="docutils">
<dt>Return the feature importances (the higher, the more important the</dt>
<dd>feature).</dd>
</dl>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns :</th><td class="field-body"><strong>feature_importances_</strong> : array, shape = [n_features]</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.fit">
<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the gradient boosting model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training vectors, where n_samples is the number of samples
and n_features is the number of features.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape = [n_samples]</p>
<blockquote>
<div><p>Target values (integers in classification, real numbers in
regression)
For classification, labels must correspond to classes
<tt class="docutils literal"><span class="pre">0,</span> <span class="pre">1,</span> <span class="pre">...,</span> <span class="pre">n_classes_-1</span></tt></p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>self</strong> : object</p>
<blockquote class="last">
<div><p>Returns self.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.get_params">
<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>deep: boolean, optional</strong> :</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.predict">
<tt class="descname">predict</tt><big>(</big><em>X</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class for X.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like of shape = [n_samples, n_features]</p>
<blockquote>
<div><p>The input samples.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>y</strong> : array of shape = [n_samples]</p>
<blockquote class="last">
<div><p>The predicted classes.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.predict_proba">
<tt class="descname">predict_proba</tt><big>(</big><em>X</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class probabilities for X.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like of shape = [n_samples, n_features]</p>
<blockquote>
<div><p>The input samples.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>p</strong> : array of shape = [n_samples]</p>
<blockquote class="last">
<div><p>The class probabilities of the input samples. Classes are
ordered by arithmetical order.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.score">
<tt class="descname">score</tt><big>(</big><em>X</em>, <em>y</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the mean accuracy on the given test data and labels.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training set.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape = [n_samples]</p>
<blockquote>
<div><p>Labels for X.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first last"><strong>z</strong> : float</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.set_params">
<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.set_params" title="Permalink 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 pipelines). The former have parameters of the form
<tt class="docutils literal"><span class="pre"><component>__<parameter></span></tt> so that it’s possible to update each
component of a nested object.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns :</th><td class="field-body"><strong>self</strong> :</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.staged_decision_function">
<tt class="descname">staged_decision_function</tt><big>(</big><em>X</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute decision function of <tt class="docutils literal"><span class="pre">X</span></tt> for each iteration.</p>
<p>This method allows monitoring (i.e. determine error on testing set)
after each stage.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like of shape = [n_samples, n_features]</p>
<blockquote>
<div><p>The input samples.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>score</strong> : generator of array, shape = [n_samples, k]</p>
<blockquote class="last">
<div><p>The decision function of the input samples. Classes are
ordered by arithmetical order. Regression and binary
classification are special cases with <tt class="docutils literal"><span class="pre">k</span> <span class="pre">==</span> <span class="pre">1</span></tt>,
otherwise <tt class="docutils literal"><span class="pre">k==n_classes</span></tt>.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.staged_predict">
<tt class="descname">staged_predict</tt><big>(</big><em>X</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class probabilities at each stage for X.</p>
<p>This method allows monitoring (i.e. determine error on testing set)
after each stage.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like of shape = [n_samples, n_features]</p>
<blockquote>
<div><p>The input samples.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>y</strong> : array of shape = [n_samples]</p>
<blockquote class="last">
<div><p>The predicted value of the input samples.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba">
<tt class="descname">staged_predict_proba</tt><big>(</big><em>X</em><big>)</big><a class="headerlink" href="#sklearn.ensemble.GradientBoostingClassifier.staged_predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class probabilities at each stage for X.</p>
<p>This method allows monitoring (i.e. determine error on testing set)
after each stage.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters :</th><td class="field-body"><p class="first"><strong>X</strong> : array-like of shape = [n_samples, n_features]</p>
<blockquote>
<div><p>The input samples.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns :</th><td class="field-body"><p class="first"><strong>y</strong> : array of shape = [n_samples]</p>
<blockquote class="last">
<div><p>The predicted value of the input samples.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
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