Rebuild dev docs at master=213a24c

Author: sklearn-ci

dev/_images/multilayerperceptron_network.png

@@ -1031,7 +1031,7 @@ See the :ref:`metrics` section of the user guide for further details.
    :no-members:
    :no-inherited-members:
 
-**User guide:** See the :ref:`neural_network` section for further details.
+**User guide:** See the :ref:`neural_networks_supervised` and :ref:`neural_networks_unsupervised` sections for further details.
 
 .. currentmodule:: sklearn
 

dev/_sources/modules/classes.txt

@@ -1,4 +1,4 @@
-.. _neural_network:
+.. _neural_networks_supervised:
 
 ==================================
 Neural network models (supervised)

dev/_sources/modules/neural_networks_supervised.txt

@@ -1,4 +1,4 @@
-.. _neural_network:
+.. _neural_networks_unsupervised:
 
 ====================================
 Neural network models (unsupervised)

dev/_sources/modules/neural_networks_unsupervised.txt

@@ -1889,7 +1889,7 @@ <h3>Multiclass and multilabel classification strategies<a class="headerlink" hre
 <span id="sklearn-neural-network-neural-network-models"></span><span id="neural-network-ref"></span><h2><a class="reference internal" href="#module-sklearn.neural_network" title="sklearn.neural_network"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.neural_network</span></tt></a>: Neural network models<a class="headerlink" href="#module-sklearn.neural_network" title="Permalink to this headline">¶</a></h2>
 <p>The <a class="reference internal" href="#module-sklearn.neural_network" title="sklearn.neural_network"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.neural_network</span></tt></a> module includes models based on neural
 networks.</p>
-<p><strong>User guide:</strong> See the <a class="reference internal" href="neural_networks_unsupervised.html#neural-network"><em>Neural network models (unsupervised)</em></a> section for further details.</p>
+<p><strong>User guide:</strong> See the <a class="reference internal" href="neural_networks_supervised.html#neural-networks-supervised"><em>Neural network models (supervised)</em></a> and <a class="reference internal" href="neural_networks_unsupervised.html#neural-networks-unsupervised"><em>Neural network models (unsupervised)</em></a> sections for further details.</p>
 <table border="1" class="longtable docutils">
 <colgroup>
 <col width="10%" />

dev/modules/classes.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></tt></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><tt class="descclassname">sklearn.base.</tt><tt class="descname">BaseEstimator</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L169"><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><tt class="descclassname">sklearn.base.</tt><tt class="descname">BaseEstimator</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L169"><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">
-<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.get_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L206"><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">
-<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.set_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L243"><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.BaseEstimator.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.base" title="sklearn.base"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.base</span></tt></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><tt class="descclassname">sklearn.base.</tt><tt class="descname">TransformerMixin</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L435"><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><tt class="descclassname">sklearn.base.</tt><tt class="descname">TransformerMixin</tt><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L435"><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">
-<tt class="descname">fit_transform</tt><big>(</big><em>X</em>, <em>y=None</em>, <em>**fit_params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L438"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.base.TransformerMixin.fit_transform" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">fit_transform</tt><big>(</big><em>X</em>, <em>y=None</em>, <em>**fit_params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L438"><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.base.TransformerMixin.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.calibration" title="sklearn.calibration"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.calibration</span></tt></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><tt class="descclassname">sklearn.calibration.</tt><tt class="descname">CalibratedClassifierCV</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/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><tt class="descclassname">sklearn.calibration.</tt><tt class="descname">CalibratedClassifierCV</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/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="r105" 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">[R105]</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="r106" 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">[R106]</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="r107" 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">[R107]</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="r108" 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">[R108]</a></td><td>Predicting Good Probabilities with Supervised Learning,
 A. Niculescu-Mizil &amp; R. Caruana, ICML 2005</td></tr>
 </tbody>
 </table>
@@ -294,12 +294,12 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 </table>
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/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>
+<tt class="descname">__init__</tt><big>(</big><em>base_estimator=None</em>, <em>method='sigmoid'</em>, <em>cv=3</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/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">
-<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/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>
+<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/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" />
@@ -331,7 +331,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.get_params">
-<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L206"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.get_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L206"><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" />
@@ -356,7 +356,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.predict">
-<tt class="descname">predict</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/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>
+<tt class="descname">predict</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/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">
@@ -381,7 +381,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.predict_proba">
-<tt class="descname">predict_proba</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/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>
+<tt class="descname">predict_proba</tt><big>(</big><em>X</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/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>
@@ -407,7 +407,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.score">
-<tt class="descname">score</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L292"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.score" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">score</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L292"><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
@@ -442,7 +442,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.calibrati
 
 <dl class="method">
 <dt id="sklearn.calibration.CalibratedClassifierCV.set_params">
-<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/base.py#L243"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.calibration.CalibratedClassifierCV.set_params" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/213a24c/sklearn/base.py#L243"><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