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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.BisectingKMeans</a><ul>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans</span></code></a><ul>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.fit"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.fit</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.fit_predict"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.fit_predict</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.fit_transform"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.fit_transform</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.get_feature_names_out"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.get_feature_names_out</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.get_metadata_routing</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.get_params"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.get_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.predict"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.predict</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.score"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.score</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_fit_request"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.set_fit_request</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_output"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.set_output</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_params"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.set_params</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_predict_request"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.set_predict_request</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_score_request"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.set_score_request</span></code></a></li>
<li><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.transform"><code class="docutils literal notranslate"><span class="pre">BisectingKMeans.transform</span></code></a></li>
</ul>
</li>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-bisectingkmeans">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.BisectingKMeans</span></code></a></li>
</ul>
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<section id="sklearn-cluster-bisectingkmeans">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.BisectingKMeans<a class="headerlink" href="#sklearn-cluster-bisectingkmeans" title="Permalink to this heading">¶</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.cluster.</span></span><span class="sig-name descname"><span class="pre">BisectingKMeans</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clusters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</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">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_init</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">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">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</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">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_x</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">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lloyd'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bisecting_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'biggest_inertia'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/cluster/_bisect_k_means.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans" title="Permalink to this definition">¶</a></dt>
<dd><p>Bisecting K-Means clustering.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#bisect-k-means"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.1.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>n_clusters</strong><span class="classifier">int, default=8</span></dt><dd><p>The number of clusters to form as well as the number of
centroids to generate.</p>
</dd>
<dt><strong>init</strong><span class="classifier">{‘k-means++’, ‘random’} or callable, default=’random’</span></dt><dd><p>Method for initialization:</p>
<p>‘k-means++’ : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.</p>
<p>‘random’: choose <code class="docutils literal notranslate"><span class="pre">n_clusters</span></code> observations (rows) at random from data
for the initial centroids.</p>
<p>If a callable is passed, it should take arguments X, n_clusters and a
random state and return an initialization.</p>
</dd>
<dt><strong>n_init</strong><span class="classifier">int, default=1</span></dt><dd><p>Number of time the inner k-means algorithm will be run with different
centroid seeds in each bisection.
That will result producing for each bisection best output of n_init
consecutive runs in terms of inertia.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines random number generation for centroid initialization
in inner K-Means. Use an int to make the randomness deterministic.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=300</span></dt><dd><p>Maximum number of iterations of the inner k-means algorithm at each
bisection.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>Verbosity mode.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=1e-4</span></dt><dd><p>Relative tolerance with regards to Frobenius norm of the difference
in the cluster centers of two consecutive iterations to declare
convergence. Used in inner k-means algorithm at each bisection to pick
best possible clusters.</p>
</dd>
<dt><strong>copy_x</strong><span class="classifier">bool, default=True</span></dt><dd><p>When pre-computing distances it is more numerically accurate to center
the data first. If copy_x is True (default), then the original data is
not modified. If False, the original data is modified, and put back
before the function returns, but small numerical differences may be
introduced by subtracting and then adding the data mean. Note that if
the original data is not C-contiguous, a copy will be made even if
copy_x is False. If the original data is sparse, but not in CSR format,
a copy will be made even if copy_x is False.</p>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{“lloyd”, “elkan”}, default=”lloyd”</span></dt><dd><p>Inner K-means algorithm used in bisection.
The classical EM-style algorithm is <code class="docutils literal notranslate"><span class="pre">"lloyd"</span></code>.
The <code class="docutils literal notranslate"><span class="pre">"elkan"</span></code> variation can be more efficient on some datasets with
well-defined clusters, by using the triangle inequality. However it’s
more memory intensive due to the allocation of an extra array of shape
<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_clusters)</span></code>.</p>
</dd>
<dt><strong>bisecting_strategy</strong><span class="classifier">{“biggest_inertia”, “largest_cluster”}, default=”biggest_inertia”</span></dt><dd><p>Defines how bisection should be performed:</p>
<blockquote>
<div><ul class="simple">
<li><dl class="simple">
<dt>“biggest_inertia” means that BisectingKMeans will always check</dt><dd><p>all calculated cluster for cluster with biggest SSE
(Sum of squared errors) and bisect it. This approach concentrates on
precision, but may be costly in terms of execution time (especially for
larger amount of data points).</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>“largest_cluster” - BisectingKMeans will always split cluster with</dt><dd><p>largest amount of points assigned to it from all clusters
previously calculated. That should work faster than picking by SSE
(‘biggest_inertia’) and may produce similar results in most cases.</p>
</dd>
</dl>
</li>
</ul>
</div></blockquote>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>cluster_centers_</strong><span class="classifier">ndarray of shape (n_clusters, n_features)</span></dt><dd><p>Coordinates of cluster centers. If the algorithm stops before fully
converging (see <code class="docutils literal notranslate"><span class="pre">tol</span></code> and <code class="docutils literal notranslate"><span class="pre">max_iter</span></code>), these will not be
consistent with <code class="docutils literal notranslate"><span class="pre">labels_</span></code>.</p>
</dd>
<dt><strong>labels_</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Labels of each point.</p>
</dd>
<dt><strong>inertia_</strong><span class="classifier">float</span></dt><dd><p>Sum of squared distances of samples to their closest cluster center,
weighted by the sample weights if provided.</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>
</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>
</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.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMeans</span></code></a></dt><dd><p>Original implementation of K-Means algorithm.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>It might be inefficient when n_cluster is less than 3, due to unnecessary
calculations for that case.</p>
<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.cluster</span> <span class="kn">import</span> <span class="n">BisectingKMeans</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">bisect_means</span> <span class="o">=</span> <span class="n">BisectingKMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</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="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">bisect_means</span><span class="o">.</span><span class="n">labels_</span>
<span class="go">array([0, 2, 0, 2, 0, 2, 1, 1, 1], dtype=int32)</span>
<span class="gp">>>> </span><span class="n">bisect_means</span><span class="o">.</span><span class="n">predict</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">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="go">array([0, 2], dtype=int32)</span>
<span class="gp">>>> </span><span class="n">bisect_means</span><span class="o">.</span><span class="n">cluster_centers_</span>
<span class="go">array([[ 2., 1.],</span>
<span class="go"> [10., 9.],</span>
<span class="go"> [10., 1.]])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.fit" title="sklearn.cluster.BisectingKMeans.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Compute bisecting k-means clustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.fit_predict" title="sklearn.cluster.BisectingKMeans.fit_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_predict</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Compute cluster centers and predict cluster index for each sample.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.fit_transform" title="sklearn.cluster.BisectingKMeans.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Compute clustering and transform X to cluster-distance space.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.get_feature_names_out" title="sklearn.cluster.BisectingKMeans.get_feature_names_out"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_feature_names_out</span></code></a>([input_features])</p></td>
<td><p>Get output feature names for transformation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.get_metadata_routing" title="sklearn.cluster.BisectingKMeans.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.get_params" title="sklearn.cluster.BisectingKMeans.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.predict" title="sklearn.cluster.BisectingKMeans.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X)</p></td>
<td><p>Predict which cluster each sample in X belongs to.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.score" title="sklearn.cluster.BisectingKMeans.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Opposite of the value of X on the K-means objective.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_fit_request" title="sklearn.cluster.BisectingKMeans.set_fit_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_fit_request</span></code></a>(*[, sample_weight])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_output" title="sklearn.cluster.BisectingKMeans.set_output"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_output</span></code></a>(*[, transform])</p></td>
<td><p>Set output container.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_params" title="sklearn.cluster.BisectingKMeans.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_predict_request" title="sklearn.cluster.BisectingKMeans.set_predict_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_predict_request</span></code></a>(*[, sample_weight])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.set_score_request" title="sklearn.cluster.BisectingKMeans.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, sample_weight])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.BisectingKMeans.transform" title="sklearn.cluster.BisectingKMeans.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X)</p></td>
<td><p>Transform X to a cluster-distance space.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.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/702316c27/sklearn/cluster/_bisect_k_means.py#L350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute bisecting k-means clustering.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training instances to cluster.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The data will be converted to C ordering,
which will cause a memory copy
if the given data is not C-contiguous.</p>
</div>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here 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>The weights for each observation in X. If None, all observations
are assigned equal weight. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is not used during
initialization if <code class="docutils literal notranslate"><span class="pre">init</span></code> is a callable.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt>self</dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.fit_predict">
<span class="sig-name descname"><span class="pre">fit_predict</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/702316c27/sklearn/cluster/_kmeans.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute cluster centers and predict cluster index for each sample.</p>
<p>Convenience method; equivalent to calling fit(X) followed by
predict(X).</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>New data to transform.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here 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>The weights for each observation in X. If None, all observations
are assigned equal weight.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Index of the cluster each sample belongs to.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.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/702316c27/sklearn/cluster/_kmeans.py#L1121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute clustering and transform X to cluster-distance space.</p>
<p>Equivalent to fit(X).transform(X), but more efficiently implemented.</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>New data to transform.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here 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>The weights for each observation in X. If None, all observations
are assigned equal weight.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_clusters)</span></dt><dd><p>X transformed in the new space.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.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/702316c27/sklearn/base.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_feature_names_out" title="Permalink to this definition">¶</a></dt>
<dd><p>Get output feature names for transformation.</p>
<p>The feature names out will prefixed by the lowercased class name. For
example, if the transformer outputs 3 features, then the feature names
out are: <code class="docutils literal notranslate"><span class="pre">["class_name0",</span> <span class="pre">"class_name1",</span> <span class="pre">"class_name2"]</span></code>.</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 <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>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Transformed feature names.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.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/702316c27/sklearn/utils/_metadata_requests.py#L1287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_metadata_routing" title="Permalink 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.cluster.BisectingKMeans.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/702316c27/sklearn/base.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.get_params" title="Permalink 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.cluster.BisectingKMeans.predict">
<span class="sig-name descname"><span class="pre">predict</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/702316c27/sklearn/cluster/_bisect_k_means.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict which cluster each sample in X belongs to.</p>
<p>Prediction is made by going down the hierarchical tree
in searching of closest leaf cluster.</p>
<p>In the vector quantization literature, <code class="docutils literal notranslate"><span class="pre">cluster_centers_</span></code> is called
the code book and each value returned by <code class="docutils literal notranslate"><span class="pre">predict</span></code> is the index of
the closest code in the code book.</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>New data to predict.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Index of the cluster each sample belongs to.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.score">
<span class="sig-name descname"><span class="pre">score</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/702316c27/sklearn/cluster/_kmeans.py#L1171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Opposite of the value of X on the K-means objective.</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>New data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here 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>The weights for each observation in X. If None, all observations
are assigned equal weight.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Opposite of the value of X on the K-means objective.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.cluster.BisectingKMeans.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/typing.html#typing.Union" title="(in Python v3.11)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><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 v3.11)"><span class="pre">None</span></a><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 v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></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.cluster.BisectingKMeans" title="sklearn.cluster._bisect_k_means.BisectingKMeans"><span class="pre">BisectingKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1077"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_fit_request" title="Permalink 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">New 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.cluster.BisectingKMeans.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/702316c27/sklearn/utils/_set_output.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_output" title="Permalink 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 simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>transform</strong><span class="classifier">{“default”, “pandas”}, 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">None</span></code>: Transform configuration is unchanged</p></li>
</ul>
</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.cluster.BisectingKMeans.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/702316c27/sklearn/base.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.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 <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.cluster.BisectingKMeans.set_predict_request">
<span class="sig-name descname"><span class="pre">set_predict_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/typing.html#typing.Union" title="(in Python v3.11)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><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 v3.11)"><span class="pre">None</span></a><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 v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></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.cluster.BisectingKMeans" title="sklearn.cluster._bisect_k_means.BisectingKMeans"><span class="pre">BisectingKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1077"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_predict_request" title="Permalink to this definition">¶</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</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">predict</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">predict</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">New 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">predict</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.cluster.BisectingKMeans.set_score_request">
<span class="sig-name descname"><span class="pre">set_score_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/typing.html#typing.Union" title="(in Python v3.11)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><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 v3.11)"><span class="pre">None</span></a><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 v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></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.cluster.BisectingKMeans" title="sklearn.cluster._bisect_k_means.BisectingKMeans"><span class="pre">BisectingKMeans</span></a></span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/702316c27/sklearn/utils/_metadata_requests.py#L1077"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.set_score_request" title="Permalink to this definition">¶</a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</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">score</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">score</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">New 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">score</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.cluster.BisectingKMeans.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/702316c27/sklearn/cluster/_kmeans.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.cluster.BisectingKMeans.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform X to a cluster-distance space.</p>
<p>In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
<code class="docutils literal notranslate"><span class="pre">transform</span></code> will typically be dense.</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>New data to transform.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_clusters)</span></dt><dd><p>X transformed in the new space.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="examples-using-sklearn-cluster-bisectingkmeans">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.BisectingKMeans</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-bisectingkmeans" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 1.1! Many bug fixes and improvements wer..."><img alt="" src="../../_images/sphx_glr_plot_release_highlights_1_1_0_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_1_1_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-1-0-py"><span class="std std-ref">Release Highlights for scikit-learn 1.1</span></a></p>
<div class="sphx-glr-thumbnail-title">Release Highlights for scikit-learn 1.1</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows differences between Regular K-Means algorithm and Bisecting K-Means."><img alt="" src="../../_images/sphx_glr_plot_bisect_kmeans_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_bisect_kmeans.html#sphx-glr-auto-examples-cluster-plot-bisect-kmeans-py"><span class="std std-ref">Bisecting K-Means and Regular K-Means Performance Comparison</span></a></p>
<div class="sphx-glr-thumbnail-title">Bisecting K-Means and Regular K-Means Performance Comparison</div>
</div></div><div class="clearer"></div></section>
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