Rebuild dev docs at master=85223b9

Author: sklearn-ci

dev/modules/generated/sklearn.base.BaseEstimator.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></code></a>.BaseEstimator<a class="headerlink" href="#sklearn-base-baseestimator" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.base.BaseEstimator">
-<em class="property">class </em><code class="descclassname">sklearn.base.</code><code class="descname">BaseEstimator</code><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L162"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">sklearn.base.</code><code class="descname">BaseEstimator</code><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L162"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all estimators in scikit-learn</p>
 <p class="rubric">Notes</p>
 <p>All estimators should specify all the parameters that can be set
@@ -201,7 +201,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.BaseEstimator.get_params">
-<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L199"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L199"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.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" />
@@ -226,7 +226,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.BaseEstimator.set_params">
-<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.set_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.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

dev/modules/generated/sklearn.base.TransformerMixin.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></code></a>.TransformerMixin<a class="headerlink" href="#sklearn-base-transformermixin" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.base.TransformerMixin">
-<em class="property">class </em><code class="descclassname">sklearn.base.</code><code class="descname">TransformerMixin</code><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L428"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">sklearn.base.</code><code class="descname">TransformerMixin</code><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L428"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mixin class for all transformers in scikit-learn.</p>
 <p class="rubric">Methods</p>
 <table border="1" class="longtable docutils">
@@ -194,7 +194,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.base" tit
 
 <dl class="method">
 <dt id="sklearn.base.TransformerMixin.fit_transform">
-<code class="descname">fit_transform</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L431"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
+<code class="descname">fit_transform</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em>, <em>**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L431"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit to data, then transform it.</p>
 <p>Fits transformer to X and y with optional parameters fit_params
 and returns a transformed version of X.</p>

dev/modules/generated/sklearn.calibration.CalibratedClassifierCV.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.calibration" title="sklearn.calibration"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.calibration</span></code></a>.CalibratedClassifierCV<a class="headerlink" href="#sklearn-calibration-calibratedclassifiercv" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.calibration.CalibratedClassifierCV">
-<em class="property">class </em><code class="descclassname">sklearn.calibration.</code><code class="descname">CalibratedClassifierCV</code><span class="sig-paren">(</span><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/calibration.py#L29"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="descclassname">sklearn.calibration.</code><code class="descname">CalibratedClassifierCV</code><span class="sig-paren">(</span><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/calibration.py#L29"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV" title="Permalink to this definition">¶</a></dt>
 <dd><p>Probability calibration with isotonic regression or sigmoid.</p>
 <p>With this class, the base_estimator is fit on the train set of the
 cross-validation generator and the test set is used for calibration.
@@ -237,31 +237,31 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 </tbody>
 </table>
 <p class="rubric">References</p>
-<table class="docutils citation" frame="void" id="r1" rules="none">
+<table class="docutils citation" frame="void" id="r12" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id1">[R1]</a></td><td>Obtaining calibrated probability estimates from decision trees
+<tr><td class="label"><a class="fn-backref" href="#id1">[R12]</a></td><td>Obtaining calibrated probability estimates from decision trees
 and naive Bayesian classifiers, B. Zadrozny &amp; C. Elkan, ICML 2001</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r2" rules="none">
+<table class="docutils citation" frame="void" id="r13" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id2">[R2]</a></td><td>Transforming Classifier Scores into Accurate Multiclass
+<tr><td class="label"><a class="fn-backref" href="#id2">[R13]</a></td><td>Transforming Classifier Scores into Accurate Multiclass
 Probability Estimates, B. Zadrozny &amp; C. Elkan, (KDD 2002)</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r3" rules="none">
+<table class="docutils citation" frame="void" id="r14" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id3">[R3]</a></td><td>Probabilistic Outputs for Support Vector Machines and Comparisons to
+<tr><td class="label"><a class="fn-backref" href="#id3">[R14]</a></td><td>Probabilistic Outputs for Support Vector Machines and Comparisons to
 Regularized Likelihood Methods, J. Platt, (1999)</td></tr>
 </tbody>
 </table>
-<table class="docutils citation" frame="void" id="r4" rules="none">
+<table class="docutils citation" frame="void" id="r15" rules="none">
 <colgroup><col class="label" /><col /></colgroup>
 <tbody valign="top">
-<tr><td class="label"><a class="fn-backref" href="#id4">[R4]</a></td><td>Predicting Good Probabilities with Supervised Learning,
+<tr><td class="label"><a class="fn-backref" href="#id4">[R15]</a></td><td>Predicting Good Probabilities with Supervised Learning,
 A. Niculescu-Mizil &amp; R. Caruana, ICML 2005</td></tr>
 </tbody>
 </table>
@@ -285,12 +285,12 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 </table>
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.__init__">
-<code class="descname">__init__</code><span class="sig-paren">(</span><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/calibration.py#L98"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.__init__" title="Permalink to this definition">¶</a></dt>
+<code class="descname">__init__</code><span class="sig-paren">(</span><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/calibration.py#L98"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.fit">
-<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/calibration.py#L103"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="Permalink to this definition">¶</a></dt>
+<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/calibration.py#L103"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.fit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fit the calibrated model</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
@@ -322,7 +322,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.get_params">
-<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L199"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L199"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.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" />
@@ -347,7 +347,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.predict">
-<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/calibration.py#L216"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="Permalink to this definition">¶</a></dt>
+<code class="descname">predict</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/calibration.py#L216"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Predict the target of new samples. Can be different from the
 prediction of the uncalibrated classifier.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -372,7 +372,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.predict_proba">
-<code class="descname">predict_proba</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/calibration.py#L186"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="Permalink to this definition">¶</a></dt>
+<code class="descname">predict_proba</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/calibration.py#L186"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.predict_proba" title="Permalink to this definition">¶</a></dt>
 <dd><p>Posterior probabilities of classification</p>
 <p>This function returns posterior probabilities of classification
 according to each class on an array of test vectors X.</p>
@@ -398,7 +398,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.score">
-<code class="descname">score</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L285"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Permalink to this definition">¶</a></dt>
+<code class="descname">score</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L285"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns 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
@@ -433,7 +433,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.set_params">
-<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="Permalink to this definition">¶</a></dt>
+<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.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

dev/modules/generated/sklearn.calibration.calibration_curve.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.calibration" title="sklearn.calibration"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.calibration</span></code></a>.calibration_curve<a class="headerlink" href="#sklearn-calibration-calibration-curve" title="Permalink to this headline">¶</a></h1>
 <dl class="function">
 <dt id="sklearn.calibration.calibration_curve">
-<code class="descclassname">sklearn.calibration.</code><code class="descname">calibration_curve</code><span class="sig-paren">(</span><em>y_true</em>, <em>y_prob</em>, <em>normalize=False</em>, <em>n_bins=5</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/276d928/sklearn/calibration.py#L491"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.calibration_curve" title="Permalink to this definition">¶</a></dt>
+<code class="descclassname">sklearn.calibration.</code><code class="descname">calibration_curve</code><span class="sig-paren">(</span><em>y_true</em>, <em>y_prob</em>, <em>normalize=False</em>, <em>n_bins=5</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/85223b9/sklearn/calibration.py#L491"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.calibration_curve" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute true and predicted probabilities for a calibration curve.</p>
 <p>Read more in the <a class="reference internal" href="../calibration.html#calibration"><span>User Guide</span></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">