deploy: 72f8315

Author: reidjohnson

_sources/user_guide/fit_predict.rst.txt

@@ -58,7 +58,7 @@ The default quantiles can be overwritten at prediction time by specifying a valu
     >>> y_pred.ndim == 1
     True
 
-The output of the `predict` method is an array with one column for each specified quantile or a single column if no quantiles are specified. The order of the output columns corresponds to the order of the quantiles, which can be specified in any order (i.e., they do not need to be monotonically ordered)::
+The output of the `predict` method is an array with one column for each specified quantile or a single column if an individual quantile is specified. The order of the output columns corresponds to the order of the quantiles, which can be specified in any order (i.e., they do not need to be monotonically ordered)::
 
     >>> y_pred = qrf.predict(X_test, quantiles=[0.5, 0.25, 0.75])
     >>> bool((y_pred[:, 0] >= y_pred[:, 1]).all())

generated/quantile_forest.ExtraTreesQuantileRegressor.html

@@ -922,7 +922,7 @@ <h1>ExtraTreesQuantileRegressor<a class="headerlink" href="#extratreesquantilere
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>y_pred</strong><span class="classifier">array of shape (n_samples, n_quantiles) or                 (n_samples, n_outputs, n_quantiles)</span></dt><dd><p>If quantiles is set to ‘mean’, then return <code class="docutils literal notranslate"><span class="pre">E(Y</span> <span class="pre">|</span> <span class="pre">X)</span></code>. Else, for
+<dt><strong>y_pred</strong><span class="classifier">array of shape (n_samples,) or (n_samples, n_quantiles) or                 (n_samples, n_outputs, n_quantiles)</span></dt><dd><p>If quantiles is set to ‘mean’, then return <code class="docutils literal notranslate"><span class="pre">E(Y</span> <span class="pre">|</span> <span class="pre">X)</span></code>. Else, for
 all quantiles, return <code class="docutils literal notranslate"><span class="pre">y</span></code> at <code class="docutils literal notranslate"><span class="pre">q</span></code> for which <code class="docutils literal notranslate"><span class="pre">F(Y=y|x)</span> <span class="pre">=</span> <span class="pre">q</span></code>,
 where <code class="docutils literal notranslate"><span class="pre">q</span></code> is the quantile.</p>
 </dd>
@@ -1008,7 +1008,7 @@ <h1>ExtraTreesQuantileRegressor<a class="headerlink" href="#extratreesquantilere
 <code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code>. If a sparse matrix is provided, it will be
 converted into a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
 </dd>
-<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples) or (n_samples, n_outputs)</span></dt><dd><p>The target values for which to calculate ranks.</p>
+<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>The target values for which to calculate ranks.</p>
 </dd>
 <dt><strong>kind</strong><span class="classifier">{“rank”, “weak”, “strict”, “mean”}, default=”rank”</span></dt><dd><p>Specifies the interpretation of the resulting score:</p>
 <ul class="simple">
@@ -1032,7 +1032,7 @@ <h1>ExtraTreesQuantileRegressor<a class="headerlink" href="#extratreesquantilere
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>y_ranks</strong><span class="classifier">array of shape (n_samples) or (n_samples, n_outputs)</span></dt><dd><p>Quantile ranks in range [0, 1].</p>
+<dt><strong>y_ranks</strong><span class="classifier">array of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>Quantile ranks in range [0, 1].</p>
 </dd>
 </dl>
 </dd>

generated/quantile_forest.RandomForestQuantileRegressor.html

@@ -933,7 +933,7 @@ <h1>RandomForestQuantileRegressor<a class="headerlink" href="#randomforestquanti
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>y_pred</strong><span class="classifier">array of shape (n_samples, n_quantiles) or                 (n_samples, n_outputs, n_quantiles)</span></dt><dd><p>If quantiles is set to ‘mean’, then return <code class="docutils literal notranslate"><span class="pre">E(Y</span> <span class="pre">|</span> <span class="pre">X)</span></code>. Else, for
+<dt><strong>y_pred</strong><span class="classifier">array of shape (n_samples,) or (n_samples, n_quantiles) or                 (n_samples, n_outputs, n_quantiles)</span></dt><dd><p>If quantiles is set to ‘mean’, then return <code class="docutils literal notranslate"><span class="pre">E(Y</span> <span class="pre">|</span> <span class="pre">X)</span></code>. Else, for
 all quantiles, return <code class="docutils literal notranslate"><span class="pre">y</span></code> at <code class="docutils literal notranslate"><span class="pre">q</span></code> for which <code class="docutils literal notranslate"><span class="pre">F(Y=y|x)</span> <span class="pre">=</span> <span class="pre">q</span></code>,
 where <code class="docutils literal notranslate"><span class="pre">q</span></code> is the quantile.</p>
 </dd>
@@ -1019,7 +1019,7 @@ <h1>RandomForestQuantileRegressor<a class="headerlink" href="#randomforestquanti
 <code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code>. If a sparse matrix is provided, it will be
 converted into a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
 </dd>
-<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples) or (n_samples, n_outputs)</span></dt><dd><p>The target values for which to calculate ranks.</p>
+<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>The target values for which to calculate ranks.</p>
 </dd>
 <dt><strong>kind</strong><span class="classifier">{“rank”, “weak”, “strict”, “mean”}, default=”rank”</span></dt><dd><p>Specifies the interpretation of the resulting score:</p>
 <ul class="simple">
@@ -1043,7 +1043,7 @@ <h1>RandomForestQuantileRegressor<a class="headerlink" href="#randomforestquanti
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><dl class="simple">
-<dt><strong>y_ranks</strong><span class="classifier">array of shape (n_samples) or (n_samples, n_outputs)</span></dt><dd><p>Quantile ranks in range [0, 1].</p>
+<dt><strong>y_ranks</strong><span class="classifier">array of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>Quantile ranks in range [0, 1].</p>
 </dd>
 </dl>
 </dd>

searchindex.js

@@ -482,7 +482,7 @@ <h2>Making Predictions<a class="headerlink" href="#making-predictions" title="Li
 <span class="go">True</span>
 </pre></div>
 </div>
-<p>The output of the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method is an array with one column for each specified quantile or a single column if no quantiles are specified. The order of the output columns corresponds to the order of the quantiles, which can be specified in any order (i.e., they do not need to be monotonically ordered):</p>
+<p>The output of the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method is an array with one column for each specified quantile or a single column if an individual quantile is specified. The order of the output columns corresponds to the order of the quantiles, which can be specified in any order (i.e., they do not need to be monotonically ordered):</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y_pred</span> <span class="o">=</span> <span class="n">qrf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">quantiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">])</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="nb">bool</span><span class="p">((</span><span class="n">y_pred</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">y_pred</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">all</span><span class="p">())</span>
 <span class="go">True</span>