docs updates [skip ci] (1384)

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: f5daf05b178977c32694cff61a7b9ed1
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dev/auto_examples/plot_MI_gigantic_hypothesis_testing_forest.html

@@ -578,8 +578,8 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
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dev/auto_examples/plot_extended_isolation_forest.html

@@ -670,7 +670,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 </aside>
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dev/auto_examples/plot_extra_oblique_random_forest.html

@@ -527,16 +527,16 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 n_repeats        1
                                     time_taken
 dataset          dimension   model
-PhishingWebsites (2000, 30)  EORF     0.959034
-                             ORF      0.956910
-cnae-9           (864, 856)  EORF     1.716336
-                             ORF      1.817993
-har              (2000, 561) EORF     1.535635
-                             ORF      2.995873
-lsvt             (100, 310)  EORF     0.822867
-                             ORF      0.877835
-wdbc             (455, 30)   EORF     0.766218
-                             ORF      0.866656
+PhishingWebsites (2000, 30)  EORF     0.897649
+                             ORF      0.947645
+cnae-9           (864, 856)  EORF     1.593771
+                             ORF      1.712286
+har              (2000, 561) EORF     1.456748
+                             ORF      2.786905
+lsvt             (100, 310)  EORF     0.816623
+                             ORF      0.857479
+wdbc             (455, 30)   EORF     0.697373
+                             ORF      0.778348
 </pre></div>
 </div>
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@@ -668,8 +668,8 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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dev/auto_examples/plot_extra_orf_sample_size.html

@@ -591,7 +591,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
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 </pre></div>
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dev/auto_examples/plot_iris_dtc.html

@@ -556,7 +556,7 @@
 <li><img src="../_images/sphx_glr_plot_iris_dtc_003.png" srcset="../_images/sphx_glr_plot_iris_dtc_003.png" alt="Axis-aligned decision tree trained on all the iris features" class = "sphx-glr-multi-img"/></li>
 <li><img src="../_images/sphx_glr_plot_iris_dtc_004.png" srcset="../_images/sphx_glr_plot_iris_dtc_004.png" alt="Oblique decision tree trained on all the iris features" class = "sphx-glr-multi-img"/></li>
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dev/auto_examples/plot_oblique_axis_aligned_forests_sparse_parity.html

@@ -528,7 +528,7 @@
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dev/auto_examples/plot_oblique_forests_iris.html

@@ -588,8 +588,8 @@
 <span class="c1"># and ExtraObliqueDecisionTree result in distinct decision boundaries.</span>
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dev/auto_examples/plot_oblique_random_forest.html

@@ -444,7 +444,7 @@
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 Loading [wdbc] dataset..
 Loading [cnae-9] dataset..
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@@ -558,7 +558,7 @@
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 </pre></div>
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dev/auto_examples/plot_overlapping_gaussians.html

@@ -620,8 +620,8 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
 <span class="c1"># curve, compared to RF which is shown below.</span>
 </pre></div>
 </div>
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-<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<img src="../_images/sphx_glr_plot_overlapping_gaussians_001.png" srcset="../_images/sphx_glr_plot_overlapping_gaussians_001.png" alt="plot overlapping gaussians" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.664 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  14 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-overlapping-gaussians-py">
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 <p><a class="reference download internal" download="" href="../_downloads/ed8294c8526ffbb9799792531e2f729f/plot_overlapping_gaussians.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_overlapping_gaussians.py</span></code></a></p>

dev/auto_examples/plot_projection_matrices.html

@@ -742,7 +742,7 @@ <h2>Discontiguous Patches<a class="headerlink" href="#discontiguous-patches" tit
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_projection_matrices_005.png" srcset="../_images/sphx_glr_plot_projection_matrices_005.png" alt="2D Discontiguous In All Dims Patch Visualization, Patch 0, Patch 1, Patch 2, Patch 3, Patch 4, Patch 5, Patch 6, Patch 7, Patch 8" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 10.795 seconds)</p>
+<img src="../_images/sphx_glr_plot_projection_matrices_005.png" srcset="../_images/sphx_glr_plot_projection_matrices_005.png" alt="2D Discontiguous In All Dims Patch Visualization, Patch 0, Patch 1, Patch 2, Patch 3, Patch 4, Patch 5, Patch 6, Patch 7, Patch 8" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 9.636 seconds)</p>
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dev/auto_examples/sg_execution_times.html

@@ -399,48 +399,48 @@
 
   <section id="computation-times">
 <span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
-<p><strong>04:55.107</strong> total execution time for <strong>auto_examples</strong> files:</p>
+<p><strong>04:43.411</strong> total execution time for <strong>auto_examples</strong> files:</p>
 <table class="table">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_extra_orf_sample_size.html#sphx-glr-auto-examples-plot-extra-orf-sample-size-py"><span class="std std-ref">Speed of Extra Oblique Random Forest vs Oblique Random Forest on different dataset sizes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_extra_orf_sample_size.py</span></code>)</p></td>
-<td><p>02:10.150</p></td>
+<td><p>02:08.839</p></td>
 <td><p>9.2 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_extra_oblique_random_forest.html#sphx-glr-auto-examples-plot-extra-oblique-random-forest-py"><span class="std std-ref">Compare extra oblique forest and oblique random forest predictions on cc18 datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_extra_oblique_random_forest.py</span></code>)</p></td>
-<td><p>00:30.683</p></td>
-<td><p>153.5 MB</p></td>
+<td><p>00:29.020</p></td>
+<td><p>136.9 MB</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_oblique_forests_iris.html#sphx-glr-auto-examples-plot-oblique-forests-iris-py"><span class="std std-ref">Compare the decision surfaces of oblique extra-trees with standard oblique trees</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_oblique_forests_iris.py</span></code>)</p></td>
-<td><p>00:29.300</p></td>
-<td><p>2365.9 MB</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_oblique_random_forest.html#sphx-glr-auto-examples-plot-oblique-random-forest-py"><span class="std std-ref">Plot oblique forest and axis-aligned random forest predictions on cc18 datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_oblique_random_forest.py</span></code>)</p></td>
+<td><p>00:28.780</p></td>
+<td><p>9.0 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_MI_gigantic_hypothesis_testing_forest.html#sphx-glr-auto-examples-plot-mi-gigantic-hypothesis-testing-forest-py"><span class="std std-ref">Mutual Information for Gigantic Hypothesis Testing (MIGHT)</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_MI_gigantic_hypothesis_testing_forest.py</span></code>)</p></td>
-<td><p>00:28.507</p></td>
-<td><p>697.0 MB</p></td>
+<td><p>00:25.958</p></td>
+<td><p>886.0 MB</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_oblique_random_forest.html#sphx-glr-auto-examples-plot-oblique-random-forest-py"><span class="std std-ref">Plot oblique forest and axis-aligned random forest predictions on cc18 datasets</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_oblique_random_forest.py</span></code>)</p></td>
-<td><p>00:28.107</p></td>
-<td><p>9.1 MB</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_oblique_forests_iris.html#sphx-glr-auto-examples-plot-oblique-forests-iris-py"><span class="std std-ref">Compare the decision surfaces of oblique extra-trees with standard oblique trees</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_oblique_forests_iris.py</span></code>)</p></td>
+<td><p>00:25.604</p></td>
+<td><p>2376.7 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_overlapping_gaussians.html#sphx-glr-auto-examples-plot-overlapping-gaussians-py"><span class="std std-ref">Plot honest forest calibrations on overlapping gaussian simulations</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_overlapping_gaussians.py</span></code>)</p></td>
-<td><p>00:12.270</p></td>
-<td><p>9.8 MB</p></td>
+<td><p>00:11.664</p></td>
+<td><p>14.2 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_extended_isolation_forest.html#sphx-glr-auto-examples-plot-extended-isolation-forest-py"><span class="std std-ref">ExtendedIsolationForest example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_extended_isolation_forest.py</span></code>)</p></td>
-<td><p>00:11.473</p></td>
-<td><p>9.6 MB</p></td>
+<td><p>00:10.572</p></td>
+<td><p>9.7 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_projection_matrices.html#sphx-glr-auto-examples-plot-projection-matrices-py"><span class="std std-ref">Plot the projection matrices of an oblique tree</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_projection_matrices.py</span></code>)</p></td>
-<td><p>00:10.795</p></td>
-<td><p>9.3 MB</p></td>
+<td><p>00:09.636</p></td>
+<td><p>9.2 MB</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="plot_oblique_axis_aligned_forests_sparse_parity.html#sphx-glr-auto-examples-plot-oblique-axis-aligned-forests-sparse-parity-py"><span class="std std-ref">Plot oblique forest and axis-aligned random forest predictions on sparse parity simulation</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_oblique_axis_aligned_forests_sparse_parity.py</span></code>)</p></td>
-<td><p>00:08.721</p></td>
+<td><p>00:08.432</p></td>
 <td><p>9.7 MB</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="plot_iris_dtc.html#sphx-glr-auto-examples-plot-iris-dtc-py"><span class="std std-ref">Plot the decision surface of decision trees trained on the iris dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_iris_dtc.py</span></code>)</p></td>
-<td><p>00:05.100</p></td>
-<td><p>9.1 MB</p></td>
+<td><p>00:04.904</p></td>
+<td><p>9.0 MB</p></td>
 </tr>
 </tbody>
 </table>

dev/whats_new/v0.2.html

@@ -424,7 +424,7 @@
 <p>This release is a major release, with many new features and improvements.
 For instance, we have added a new implementation of the extended isolation forest,
 enabled all decision trees to take advantage of <code class="docutils literal notranslate"><span class="pre">partial_fit</span></code> meaning trees have streaming
-capabilities. Moreover, we have added an analagous implementation of extra-trees for oblique-trees.
+capabilities. Moreover, we have added an analogous implementation of extra-trees for oblique-trees.
 Finally, this release includes a highly experimental feature for multivariate high-dimensional
 hypothesis testing using permutation forests and a feature importance testing forest.</p>
 <section id="changelog">