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  <section id="baseestimator">
<h1>BaseEstimator<a class="headerlink" href="#baseestimator" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.base.BaseEstimator">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">BaseEstimator</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/base.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator" title="Link to this definition">#</a></dt>
<dd><p>Base class for all estimators in scikit-learn.</p>
<p>Inheriting from this class provides default implementations of:</p>
<ul class="simple">
<li><p>setting and getting parameters used by <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> and friends;</p></li>
<li><p>textual and HTML representation displayed in terminals and IDEs;</p></li>
<li><p>estimator serialization;</p></li>
<li><p>parameters validation;</p></li>
<li><p>data validation;</p></li>
<li><p>feature names validation.</p></li>
</ul>
<p>Read more in the <a class="reference internal" href="../../developers/develop.html#rolling-your-own-estimator"><span class="std std-ref">User Guide</span></a>.</p>
<p class="rubric">Notes</p>
<p>All estimators should specify all the parameters that can be set
at the class level in their <code class="docutils literal notranslate"><span class="pre">__init__</span></code> as explicit keyword
arguments (no <code class="docutils literal notranslate"><span class="pre">*args</span></code> or <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>).</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">class</span><span class="w"> </span><span class="nc">MyEstimator</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="gp">... </span>    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">param</span> <span class="o">=</span> <span class="n">param</span>
<span class="gp">... </span>    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">is_fitted_</span> <span class="o">=</span> <span class="kc">True</span>
<span class="gp">... </span>        <span class="k">return</span> <span class="bp">self</span>
<span class="gp">... </span>    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="gp">... </span>        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">fill_value</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">param</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</span> <span class="o">=</span> <span class="n">MyEstimator</span><span class="p">(</span><span class="n">param</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
<span class="go">{&#39;param&#39;: 2}</span>
<span class="gp">&gt;&gt;&gt; </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">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</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">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</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="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([2, 2, 2])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">param</span><span class="o">=</span><span class="mi">3</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="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([3, 3, 3])</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.base.BaseEstimator.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/b54e4deea/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.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.base.BaseEstimator.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/b54e4deea/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.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.base.BaseEstimator.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/b54e4deea/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.BaseEstimator.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">&lt;component&gt;__&lt;parameter&gt;</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>

</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="Clustering can be expensive, especially when our dataset contains millions of datapoints. Many clustering algorithms are not inductive and so cannot be directly applied to new data samples without recomputing the clustering, which may be intractable. Instead, we can use clustering to then learn an inductive model with a classifier, which has several benefits:"><img alt="" src="../../_images/sphx_glr_plot_inductive_clustering_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py"><span class="std std-ref">Inductive Clustering</span></a></p>
  <div class="sphx-glr-thumbnail-title">Inductive Clustering</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The __sklearn_is_fitted__ method is a convention used in scikit-learn for checking whether an estimator object has been fitted or not. This method is typically implemented in custom estimator classes that are built on top of scikit-learn&#x27;s base classes like BaseEstimator or its subclasses."><img alt="" src="../../_images/sphx_glr_sklearn_is_fitted_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/developing_estimators/sklearn_is_fitted.html#sphx-glr-auto-examples-developing-estimators-sklearn-is-fitted-py"><span class="std std-ref">__sklearn_is_fitted__ as Developer API</span></a></p>
  <div class="sphx-glr-thumbnail-title">__sklearn_is_fitted__ as Developer API</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This document shows how you can use the metadata routing mechanism &lt;metadata_routing&gt; in scikit-learn to route metadata to the estimators, scorers, and CV splitters consuming them."><img alt="" src="../../_images/sphx_glr_plot_metadata_routing_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_metadata_routing.html#sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Metadata Routing</span></a></p>
  <div class="sphx-glr-thumbnail-title">Metadata Routing</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example presents how to chain KNeighborsTransformer and TSNE in a pipeline. It also shows how to wrap the packages nmslib and pynndescent to replace KNeighborsTransformer and perform approximate nearest neighbors. These packages can be installed with pip install nmslib pynndescent."><img alt="" src="../../_images/sphx_glr_approximate_nearest_neighbors_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/neighbors/approximate_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-approximate-nearest-neighbors-py"><span class="std std-ref">Approximate nearest neighbors in TSNE</span></a></p>
  <div class="sphx-glr-thumbnail-title">Approximate nearest neighbors in TSNE</div>
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