Rebuild dev docs at master=b4adafe

Author: sklearn-ci

dev/auto_examples/ensemble/plot_partial_dependence.html

@@ -176,7 +176,7 @@
 (see <a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor.feature_importances_" title="sklearn.ensemble.GradientBoostingRegressor.feature_importances_"><tt class="xref py py-attr docutils literal"><span class="pre">feature_importances_</span></tt></a>).</p>
 <p>This example shows how to obtain partial dependence plots from a
 <a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#sklearn.ensemble.GradientBoostingRegressor" title="sklearn.ensemble.GradientBoostingRegressor"><tt class="xref py py-class docutils literal"><span class="pre">GradientBoostingRegressor</span></tt></a> trained on the California
-housing dataset. The example is taken from <a class="reference internal" href="../../modules/ensemble.html#htf2009" id="id2">[HTF2009]</a>.</p>
+housing dataset. The example is taken from <a class="reference internal" href="#htf2009" id="id2">[HTF2009]</a>.</p>
 <p>The plot shows four one-way and one two-way partial dependence plots.
 The target variables for the one-way PDP are:
 median income (<cite>MedInc</cite>), avg. occupants per household (<cite>AvgOccup</cite>),

dev/modules/generated/sklearn.cluster.AgglomerativeClustering.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></a>.AgglomerativeClustering<a class="headerlink" href="#sklearn-cluster-agglomerativeclustering" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.cluster.AgglomerativeClustering">
-<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">AgglomerativeClustering</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x1d4e1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L583"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">AgglomerativeClustering</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x18bd1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L583"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering" title="Permalink to this definition">¶</a></dt>
 <dd><p>Agglomerative Clustering</p>
 <p>Recursively merges the pair of clusters that minimally increases
 a given linkage distance.</p>
@@ -284,7 +284,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.AgglomerativeClustering.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x1d4e1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x18bd1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.AgglomerativeClustering.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">

dev/modules/generated/sklearn.cluster.FeatureAgglomeration.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.cluster</span></tt></a>.FeatureAgglomeration<a class="headerlink" href="#sklearn-cluster-featureagglomeration" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.cluster.FeatureAgglomeration">
-<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">FeatureAgglomeration</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x1d4e1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L746"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.cluster.</tt><tt class="descname">FeatureAgglomeration</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x18bd1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L746"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration" title="Permalink to this definition">¶</a></dt>
 <dd><p>Agglomerate features.</p>
 <p>Similar to AgglomerativeClustering, but recursively merges features
 instead of samples.</p>
@@ -293,7 +293,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.cluster"
 </table>
 <dl class="method">
 <dt id="sklearn.cluster.FeatureAgglomeration.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x1d4e1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>n_clusters=2</em>, <em>affinity='euclidean'</em>, <em>memory=Memory(cachedir=None)</em>, <em>connectivity=None</em>, <em>compute_full_tree='auto'</em>, <em>linkage='ward'</em>, <em>pooling_func=&lt;function mean at 0x18bd1b8&gt;</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/cluster/hierarchical.py#L660"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.FeatureAgglomeration.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">

dev/modules/generated/sklearn.feature_selection.SelectKBest.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.feature_selection</span></tt></a>.SelectKBest<a class="headerlink" href="#sklearn-feature-selection-selectkbest" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.feature_selection.SelectKBest">
-<em class="property">class </em><tt class="descclassname">sklearn.feature_selection.</tt><tt class="descname">SelectKBest</tt><big>(</big><em>score_func=&lt;function f_classif at 0x59728c0&gt;</em>, <em>k=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L412"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.feature_selection.</tt><tt class="descname">SelectKBest</tt><big>(</big><em>score_func=&lt;function f_classif at 0x549f938&gt;</em>, <em>k=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L412"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Select features according to the k highest scores.</p>
 <p>Read more in the <a class="reference internal" href="../feature_selection.html#univariate-feature-selection"><em>User Guide</em></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -259,7 +259,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.feature_s
 </table>
 <dl class="method">
 <dt id="sklearn.feature_selection.SelectKBest.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>score_func=&lt;function f_classif at 0x59728c0&gt;</em>, <em>k=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L452"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>score_func=&lt;function f_classif at 0x549f938&gt;</em>, <em>k=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L452"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectKBest.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">

dev/modules/generated/sklearn.feature_selection.SelectPercentile.html

@@ -172,7 +172,7 @@
 <h1><a class="reference internal" href="../classes.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.feature_selection</span></tt></a>.SelectPercentile<a class="headerlink" href="#sklearn-feature-selection-selectpercentile" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="sklearn.feature_selection.SelectPercentile">
-<em class="property">class </em><tt class="descclassname">sklearn.feature_selection.</tt><tt class="descname">SelectPercentile</tt><big>(</big><em>score_func=&lt;function f_classif at 0x59728c0&gt;</em>, <em>percentile=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L343"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><tt class="descclassname">sklearn.feature_selection.</tt><tt class="descname">SelectPercentile</tt><big>(</big><em>score_func=&lt;function f_classif at 0x549f938&gt;</em>, <em>percentile=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L343"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Select features according to a percentile of the highest scores.</p>
 <p>Read more in the <a class="reference internal" href="../feature_selection.html#univariate-feature-selection"><em>User Guide</em></a>.</p>
 <table class="docutils field-list" frame="void" rules="none">
@@ -258,7 +258,7 @@ <h1><a class="reference internal" href="../classes.html#module-sklearn.feature_s
 </table>
 <dl class="method">
 <dt id="sklearn.feature_selection.SelectPercentile.__init__">
-<tt class="descname">__init__</tt><big>(</big><em>score_func=&lt;function f_classif at 0x59728c0&gt;</em>, <em>percentile=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L382"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.__init__" title="Permalink to this definition">¶</a></dt>
+<tt class="descname">__init__</tt><big>(</big><em>score_func=&lt;function f_classif at 0x549f938&gt;</em>, <em>percentile=10</em><big>)</big><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b4adafe/sklearn/feature_selection/univariate_selection.py#L382"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.SelectPercentile.__init__" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="method">