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<section id="randomtreesembedding">
<h1>RandomTreesEmbedding<a class="headerlink" href="#randomtreesembedding" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.ensemble.</span></span><span class="sig-name descname"><span class="pre">RandomTreesEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_weight_fraction_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_impurity_decrease</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_forest.py#L2656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding" title="Link to this definition">#</a></dt>
<dd><p>An ensemble of totally random trees.</p>
<p>An unsupervised transformation of a dataset to a high-dimensional
sparse representation. A datapoint is coded according to which leaf of
each tree it is sorted into. Using a one-hot encoding of the leaves,
this leads to a binary coding with as many ones as there are trees in
the forest.</p>
<p>The dimensionality of the resulting representation is
<code class="docutils literal notranslate"><span class="pre">n_out</span> <span class="pre"><=</span> <span class="pre">n_estimators</span> <span class="pre">*</span> <span class="pre">max_leaf_nodes</span></code>. If <code class="docutils literal notranslate"><span class="pre">max_leaf_nodes</span> <span class="pre">==</span> <span class="pre">None</span></code>,
the number of leaf nodes is at most <code class="docutils literal notranslate"><span class="pre">n_estimators</span> <span class="pre">*</span> <span class="pre">2</span> <span class="pre">**</span> <span class="pre">max_depth</span></code>.</p>
<p>Read more in the <a class="reference internal" href="../ensemble.html#random-trees-embedding"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>n_estimators</strong><span class="classifier">int, default=100</span></dt><dd><p>Number of trees in the forest.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.22: </span>The default value of <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code> changed from 10 to 100
in 0.22.</p>
</div>
</dd>
<dt><strong>max_depth</strong><span class="classifier">int, default=5</span></dt><dd><p>The maximum depth of each tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.</p>
</dd>
<dt><strong>min_samples_split</strong><span class="classifier">int or float, default=2</span></dt><dd><p>The minimum number of samples required to split an internal node:</p>
<ul class="simple">
<li><p>If int, then consider <code class="docutils literal notranslate"><span class="pre">min_samples_split</span></code> as the minimum number.</p></li>
<li><p>If float, then <code class="docutils literal notranslate"><span class="pre">min_samples_split</span></code> is a fraction and
<code class="docutils literal notranslate"><span class="pre">ceil(min_samples_split</span> <span class="pre">*</span> <span class="pre">n_samples)</span></code> is the minimum
number of samples for each split.</p></li>
</ul>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.18: </span>Added float values for fractions.</p>
</div>
</dd>
<dt><strong>min_samples_leaf</strong><span class="classifier">int or float, default=1</span></dt><dd><p>The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least <code class="docutils literal notranslate"><span class="pre">min_samples_leaf</span></code> training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.</p>
<ul class="simple">
<li><p>If int, then consider <code class="docutils literal notranslate"><span class="pre">min_samples_leaf</span></code> as the minimum number.</p></li>
<li><p>If float, then <code class="docutils literal notranslate"><span class="pre">min_samples_leaf</span></code> is a fraction and
<code class="docutils literal notranslate"><span class="pre">ceil(min_samples_leaf</span> <span class="pre">*</span> <span class="pre">n_samples)</span></code> is the minimum
number of samples for each node.</p></li>
</ul>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.18: </span>Added float values for fractions.</p>
</div>
</dd>
<dt><strong>min_weight_fraction_leaf</strong><span class="classifier">float, default=0.0</span></dt><dd><p>The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.</p>
</dd>
<dt><strong>max_leaf_nodes</strong><span class="classifier">int, default=None</span></dt><dd><p>Grow trees with <code class="docutils literal notranslate"><span class="pre">max_leaf_nodes</span></code> in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.</p>
</dd>
<dt><strong>min_impurity_decrease</strong><span class="classifier">float, default=0.0</span></dt><dd><p>A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.</p>
<p>The weighted impurity decrease equation is the following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">N_t</span> <span class="o">/</span> <span class="n">N</span> <span class="o">*</span> <span class="p">(</span><span class="n">impurity</span> <span class="o">-</span> <span class="n">N_t_R</span> <span class="o">/</span> <span class="n">N_t</span> <span class="o">*</span> <span class="n">right_impurity</span>
<span class="o">-</span> <span class="n">N_t_L</span> <span class="o">/</span> <span class="n">N_t</span> <span class="o">*</span> <span class="n">left_impurity</span><span class="p">)</span>
</pre></div>
</div>
<p>where <code class="docutils literal notranslate"><span class="pre">N</span></code> is the total number of samples, <code class="docutils literal notranslate"><span class="pre">N_t</span></code> is the number of
samples at the current node, <code class="docutils literal notranslate"><span class="pre">N_t_L</span></code> is the number of samples in the
left child, and <code class="docutils literal notranslate"><span class="pre">N_t_R</span></code> is the number of samples in the right child.</p>
<p><code class="docutils literal notranslate"><span class="pre">N</span></code>, <code class="docutils literal notranslate"><span class="pre">N_t</span></code>, <code class="docutils literal notranslate"><span class="pre">N_t_R</span></code> and <code class="docutils literal notranslate"><span class="pre">N_t_L</span></code> all refer to the weighted sum,
if <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is passed.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.19.</span></p>
</div>
</dd>
<dt><strong>sparse_output</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether or not to return a sparse CSR matrix, as default behavior,
or to return a dense array compatible with dense pipeline operators.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of jobs to run in parallel. <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.fit" title="sklearn.ensemble.RandomTreesEmbedding.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>, <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.transform" title="sklearn.ensemble.RandomTreesEmbedding.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>,
<a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.decision_path" title="sklearn.ensemble.RandomTreesEmbedding.decision_path"><code class="xref py py-meth docutils literal notranslate"><span class="pre">decision_path</span></code></a> and <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.apply" title="sklearn.ensemble.RandomTreesEmbedding.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply</span></code></a> are all parallelized over the
trees. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_backend.html#joblib.parallel_backend" title="(in joblib v1.5.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a>
context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n_jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the generation of the random <code class="docutils literal notranslate"><span class="pre">y</span></code> used to fit the trees
and the draw of the splits for each feature at the trees’ nodes.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a> for details.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>Controls the verbosity when fitting and predicting.</p>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool, default=False</span></dt><dd><p>When set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See <a class="reference internal" href="../../glossary.html#term-warm_start"><span class="xref std std-term">Glossary</span></a> and
<a class="reference internal" href="../ensemble.html#tree-ensemble-warm-start"><span class="std std-ref">Fitting additional trees</span></a> for details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>estimator_</strong><span class="classifier"><a class="reference internal" href="sklearn.tree.ExtraTreeRegressor.html#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree.ExtraTreeRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExtraTreeRegressor</span></code></a> instance</span></dt><dd><p>The child estimator template used to create the collection of fitted
sub-estimators.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.2: </span><code class="docutils literal notranslate"><span class="pre">base_estimator_</span></code> was renamed to <code class="docutils literal notranslate"><span class="pre">estimator_</span></code>.</p>
</div>
</dd>
<dt><strong>estimators_</strong><span class="classifier">list of <a class="reference internal" href="sklearn.tree.ExtraTreeRegressor.html#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree.ExtraTreeRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExtraTreeRegressor</span></code></a> instances</span></dt><dd><p>The collection of fitted sub-estimators.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.feature_importances_" title="sklearn.ensemble.RandomTreesEmbedding.feature_importances_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_importances_</span></code></a><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>The impurity-based feature importances.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
<dt><strong>n_outputs_</strong><span class="classifier">int</span></dt><dd><p>The number of outputs when <code class="docutils literal notranslate"><span class="pre">fit</span></code> is performed.</p>
</dd>
<dt><strong>one_hot_encoder_</strong><span class="classifier">OneHotEncoder instance</span></dt><dd><p>One-hot encoder used to create the sparse embedding.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.estimators_samples_" title="sklearn.ensemble.RandomTreesEmbedding.estimators_samples_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimators_samples_</span></code></a><span class="classifier">list of arrays</span></dt><dd><p>The subset of drawn samples for each base estimator.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier" title="sklearn.ensemble.ExtraTreesClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExtraTreesClassifier</span></code></a></dt><dd><p>An extra-trees classifier.</p>
</dd>
<dt><a class="reference internal" href="sklearn.ensemble.ExtraTreesRegressor.html#sklearn.ensemble.ExtraTreesRegressor" title="sklearn.ensemble.ExtraTreesRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExtraTreesRegressor</span></code></a></dt><dd><p>An extra-trees regressor.</p>
</dd>
<dt><a class="reference internal" href="sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a></dt><dd><p>A random forest classifier.</p>
</dd>
<dt><a class="reference internal" href="sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomForestRegressor</span></code></a></dt><dd><p>A random forest regressor.</p>
</dd>
<dt><a class="reference internal" href="sklearn.tree.ExtraTreeClassifier.html#sklearn.tree.ExtraTreeClassifier" title="sklearn.tree.ExtraTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.tree.ExtraTreeClassifier</span></code></a></dt><dd><p>An extremely randomized tree classifier.</p>
</dd>
<dt><a class="reference internal" href="sklearn.tree.ExtraTreeRegressor.html#sklearn.tree.ExtraTreeRegressor" title="sklearn.tree.ExtraTreeRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sklearn.tree.ExtraTreeRegressor</span></code></a></dt><dd><p>An extremely randomized tree regressor.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r6e47e53bacbd-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”,
Machine Learning, 63(1), 3-42, 2006.</p>
</div>
<div class="citation" id="r6e47e53bacbd-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>Moosmann, F. and Triggs, B. and Jurie, F. “Fast discriminative
visual codebooks using randomized clustering forests”
NIPS 2007</p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomTreesEmbedding</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">random_trees</span> <span class="o">=</span> <span class="n">RandomTreesEmbedding</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_sparse_embedding</span> <span class="o">=</span> <span class="n">random_trees</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_sparse_embedding</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],</span>
<span class="go"> [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],</span>
<span class="go"> [0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],</span>
<span class="go"> [1., 0., 1., 0., 1., 0., 1., 0., 1., 0.],</span>
<span class="go"> [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]])</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.apply">
<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_forest.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.apply" title="Link to this definition">#</a></dt>
<dd><p>Apply trees in the forest to X, return leaf indices.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input samples. Internally, its dtype will be converted to
<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>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_leaves</strong><span class="classifier">ndarray of shape (n_samples, n_estimators)</span></dt><dd><p>For each datapoint x in X and for each tree in the forest,
return the index of the leaf x ends up in.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.decision_path">
<span class="sig-name descname"><span class="pre">decision_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_forest.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.decision_path" title="Link to this definition">#</a></dt>
<dd><p>Return the decision path in the forest.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.18.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input samples. Internally, its dtype will be converted to
<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>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>indicator</strong><span class="classifier">sparse matrix of shape (n_samples, n_nodes)</span></dt><dd><p>Return a node indicator matrix where non zero elements indicates
that the samples goes through the nodes. The matrix is of CSR
format.</p>
</dd>
<dt><strong>n_nodes_ptr</strong><span class="classifier">ndarray of shape (n_estimators + 1,)</span></dt><dd><p>The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
gives the indicator value for the i-th estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.estimators_samples_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">estimators_samples_</span></span><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.estimators_samples_" title="Link to this definition">#</a></dt>
<dd><p>The subset of drawn samples for each base estimator.</p>
<p>Returns a dynamically generated list of indices identifying
the samples used for fitting each member of the ensemble, i.e.,
the in-bag samples.</p>
<p>Note: the list is re-created at each call to the property in order
to reduce the object memory footprint by not storing the sampling
data. Thus fetching the property may be slower than expected.</p>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.feature_importances_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">feature_importances_</span></span><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.feature_importances_" title="Link to this definition">#</a></dt>
<dd><p>The impurity-based feature importances.</p>
<p>The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.</p>
<p>Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
<a class="reference internal" href="sklearn.inspection.permutation_importance.html#sklearn.inspection.permutation_importance" title="sklearn.inspection.permutation_importance"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.inspection.permutation_importance</span></code></a> as an alternative.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>feature_importances_</strong><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>The values of this array sum to 1, unless all trees are single node
trees consisting of only the root node, in which case it will be an
array of zeros.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_forest.py#L2901"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input samples. Use <code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> for maximum
efficiency. Sparse matrices are also supported, use sparse
<code class="docutils literal notranslate"><span class="pre">csc_matrix</span></code> for maximum efficiency.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns the instance itself.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.fit_transform">
<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_forest.py#L2931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.fit_transform" title="Link to this definition">#</a></dt>
<dd><p>Fit estimator and transform dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Input data used to build forests. Use <code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> for
maximum efficiency.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_transformed</strong><span class="classifier">sparse matrix of shape (n_samples, n_out)</span></dt><dd><p>Transformed dataset.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out">
<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_forest.py#L2966"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out" title="Link to this definition">#</a></dt>
<dd><p>Get output feature names for transformation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>input_features</strong><span class="classifier">array-like of str or None, default=None</span></dt><dd><p>Only used to validate feature names with the names seen in <a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding.fit" title="sklearn.ensemble.RandomTreesEmbedding.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Transformed feature names, in the format of
<code class="docutils literal notranslate"><span class="pre">randomtreesembedding_{tree}_{leaf}</span></code>, where <code class="docutils literal notranslate"><span class="pre">tree</span></code> is the tree used
to generate the leaf and <code class="docutils literal notranslate"><span class="pre">leaf</span></code> is the index of a leaf node
in that tree. Note that the node indexing scheme is used to
index both nodes with children (split nodes) and leaf nodes.
Only the latter can be present as output features.
As a consequence, there are missing indices in the output
feature names.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.set_fit_request">
<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble._forest.RandomTreesEmbedding"><span class="pre">RandomTreesEmbedding</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_metadata_requests.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_fit_request" title="Link to this definition">#</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <a class="reference internal" href="sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config</span></code></a>).
Please see <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.set_output">
<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/utils/_set_output.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_output" title="Link to this definition">#</a></dt>
<dd><p>Set output container.</p>
<p>See <a class="reference internal" href="../../auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
for an example on how to use the API.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>transform</strong><span class="classifier">{“default”, “pandas”, “polars”}, default=None</span></dt><dd><p>Configure output of <code class="docutils literal notranslate"><span class="pre">transform</span></code> and <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"default"</span></code>: Default output format of a transformer</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"pandas"</span></code>: DataFrame output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"polars"</span></code>: Polars output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: Transform configuration is unchanged</p></li>
</ul>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4: </span><code class="docutils literal notranslate"><span class="pre">"polars"</span></code> option was added.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/base.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.set_params" title="Link 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 <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.ensemble.RandomTreesEmbedding.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/ensemble/_forest.py#L2998"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.ensemble.RandomTreesEmbedding.transform" title="Link to this definition">#</a></dt>
<dd><p>Transform dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Input data to be transformed. Use <code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> for maximum
efficiency. Sparse matrices are also supported, use sparse
<code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code> for maximum efficiency.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_transformed</strong><span class="classifier">sparse matrix of shape (n_samples, n_out)</span></dt><dd><p>Transformed dataset.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Transform your features into a higher dimensional, sparse space. Then train a linear model on these features."><img alt="" src="../../_images/sphx_glr_plot_feature_transformation_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">Feature transformations with ensembles of trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Feature transformations with ensembles of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representation, which might be beneficial for classification. The mapping is completely unsupervised and very efficient."><img alt="" src="../../_images/sphx_glr_plot_random_forest_embedding_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_random_forest_embedding.html#sphx-glr-auto-examples-ensemble-plot-random-forest-embedding-py"><span class="std std-ref">Hashing feature transformation using Totally Random Trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Hashing feature transformation using Totally Random Trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We illustrate various embedding techniques on the digits dataset."><img alt="" src="../../_images/sphx_glr_plot_lle_digits_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py"><span class="std std-ref">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</span></a></p>
<div class="sphx-glr-thumbnail-title">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...</div>
</div></div></section>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding"><code class="docutils literal notranslate"><span class="pre">RandomTreesEmbedding</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.apply"><code class="docutils literal notranslate"><span class="pre">apply</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.decision_path"><code class="docutils literal notranslate"><span class="pre">decision_path</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.estimators_samples_"><code class="docutils literal notranslate"><span class="pre">estimators_samples_</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.feature_importances_"><code class="docutils literal notranslate"><span class="pre">feature_importances_</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.fit_transform"><code class="docutils literal notranslate"><span class="pre">fit_transform</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.get_feature_names_out"><code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.set_fit_request"><code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.ensemble.RandomTreesEmbedding.transform"><code class="docutils literal notranslate"><span class="pre">transform</span></code></a></li>
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