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<section id="powertransformer">
<h1>PowerTransformer<a class="headerlink" href="#powertransformer" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">PowerTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'yeo-johnson'</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">standardize</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">copy</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/preprocessing/_data.py#L3158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer" title="Link to this definition">#</a></dt>
<dd><p>Apply a power transform featurewise to make data more Gaussian-like.</p>
<p>Power transforms are a family of parametric, monotonic transformations
that are applied to make data more Gaussian-like. This is useful for
modeling issues related to heteroscedasticity (non-constant variance),
or other situations where normality is desired.</p>
<p>Currently, PowerTransformer supports the Box-Cox transform and the
Yeo-Johnson transform. The optimal parameter for stabilizing variance and
minimizing skewness is estimated through maximum likelihood.</p>
<p>Box-Cox requires input data to be strictly positive, while Yeo-Johnson
supports both positive or negative data.</p>
<p>By default, zero-mean, unit-variance normalization is applied to the
transformed data.</p>
<p>For an example visualization, refer to <a class="reference internal" href="../../auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-power-transformer-section"><span class="std std-ref">Compare PowerTransformer with
other scalers</span></a>. To see the
effect of Box-Cox and Yeo-Johnson transformations on different
distributions, see:
<a class="reference internal" href="../../auto_examples/preprocessing/plot_map_data_to_normal.html#sphx-glr-auto-examples-preprocessing-plot-map-data-to-normal-py"><span class="std std-ref">Map data to a normal distribution</span></a>.</p>
<p>Read more in the <a class="reference internal" href="../preprocessing.html#preprocessing-transformer"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.20.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>method</strong><span class="classifier">{‘yeo-johnson’, ‘box-cox’}, default=’yeo-johnson’</span></dt><dd><p>The power transform method. Available methods are:</p>
<ul class="simple">
<li><p>‘yeo-johnson’ <a class="reference internal" href="#rf3e1504535de-1" id="id1">[1]</a>, works with positive and negative values</p></li>
<li><p>‘box-cox’ <a class="reference internal" href="#rf3e1504535de-2" id="id2">[2]</a>, only works with strictly positive values</p></li>
</ul>
</dd>
<dt><strong>standardize</strong><span class="classifier">bool, default=True</span></dt><dd><p>Set to True to apply zero-mean, unit-variance normalization to the
transformed output.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, default=True</span></dt><dd><p>Set to False to perform inplace computation during transformation.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>lambdas_</strong><span class="classifier">ndarray of float of shape (n_features,)</span></dt><dd><p>The parameters of the power transformation for the selected features.</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>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.preprocessing.power_transform.html#sklearn.preprocessing.power_transform" title="sklearn.preprocessing.power_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">power_transform</span></code></a></dt><dd><p>Equivalent function without the estimator API.</p>
</dd>
<dt><a class="reference internal" href="sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer" title="sklearn.preprocessing.QuantileTransformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">QuantileTransformer</span></code></a></dt><dd><p>Maps data to a standard normal distribution with the parameter <code class="docutils literal notranslate"><span class="pre">output_distribution='normal'</span></code>.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>NaNs are treated as missing values: disregarded in <code class="docutils literal notranslate"><span class="pre">fit</span></code>, and maintained
in <code class="docutils literal notranslate"><span class="pre">transform</span></code>.</p>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="rf3e1504535de-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://doi.org/10.1093/biomet/87.4.954">I.K. Yeo and R.A. Johnson, “A new family of power
transformations to improve normality or symmetry.” Biometrika,
87(4), pp.954-959, (2000).</a></p>
</div>
<div class="citation" id="rf3e1504535de-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
<p><a class="reference external" href="https://doi.org/10.1111/j.2517-6161.1964.tb00553.x">G.E.P. Box and D.R. Cox, “An Analysis of Transformations”,
Journal of the Royal Statistical Society B, 26, 211-252 (1964).</a></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">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">PowerTransformer</span>
<span class="gp">>>> </span><span class="n">pt</span> <span class="o">=</span> <span class="n">PowerTransformer</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</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">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">pt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
<span class="go">PowerTransformer()</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">pt</span><span class="o">.</span><span class="n">lambdas_</span><span class="p">)</span>
<span class="go">[ 1.386... -3.100...]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">pt</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
<span class="go">[[-1.316... -0.707...]</span>
<span class="go"> [ 0.209... -0.707...]</span>
<span class="go"> [ 1.106... 1.414...]]</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/preprocessing/_data.py#L3267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.fit" title="Link to this definition">#</a></dt>
<dd><p>Estimate the optimal parameter lambda for each feature.</p>
<p>The optimal lambda parameter for minimizing skewness is estimated on
each feature independently using maximum likelihood.</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 of shape (n_samples, n_features)</span></dt><dd><p>The data used to estimate the optimal transformation parameters.</p>
</dd>
<dt><strong>y</strong><span class="classifier">None</span></dt><dd><p>Ignored.</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>Fitted transformer.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/4ee3afa55/sklearn/preprocessing/_data.py#L3290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.fit_transform" title="Link to this definition">#</a></dt>
<dd><p>Fit <code class="docutils literal notranslate"><span class="pre">PowerTransformer</span></code> to <code class="docutils literal notranslate"><span class="pre">X</span></code>, then transform <code class="docutils literal notranslate"><span class="pre">X</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>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data used to estimate the optimal transformation parameters
and to be transformed using a power transformation.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</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_features)</span></dt><dd><p>Transformed data.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.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/base.py#L846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.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>Input features.</p>
<ul class="simple">
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is
used as feature names in. If <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is not defined,
then the following input feature names are generated:
<code class="docutils literal notranslate"><span class="pre">["x0",</span> <span class="pre">"x1",</span> <span class="pre">...,</span> <span class="pre">"x(n_features_in_</span> <span class="pre">-</span> <span class="pre">1)"]</span></code>.</p></li>
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is an array-like, then <code class="docutils literal notranslate"><span class="pre">input_features</span></code> must
match <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> if <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is defined.</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>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Same as input features.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.inverse_transform">
<span class="sig-name descname"><span class="pre">inverse_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/preprocessing/_data.py#L3383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.inverse_transform" title="Link to this definition">#</a></dt>
<dd><p>Apply the inverse power transformation using the fitted lambdas.</p>
<p>The inverse of the Box-Cox transformation is given by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">lambda_</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_trans</span> <span class="o">*</span> <span class="n">lambda_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">lambda_</span><span class="p">)</span>
</pre></div>
</div>
<p>The inverse of the Yeo-Johnson transformation is given by:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">X</span> <span class="o">>=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
<span class="k">elif</span> <span class="n">X</span> <span class="o">>=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">X</span> <span class="o">=</span> <span class="p">(</span><span class="n">X_trans</span> <span class="o">*</span> <span class="n">lambda_</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">lambda_</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
<span class="k">elif</span> <span class="n">X</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">X</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">lambda_</span><span class="p">)</span> <span class="o">*</span> <span class="n">X_trans</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">lambda_</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">X</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">lambda_</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">X</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">X_trans</span><span class="p">)</span>
</pre></div>
</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 of shape (n_samples, n_features)</span></dt><dd><p>The transformed data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>The original data.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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/preprocessing/_data.py#L3354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.PowerTransformer.transform" title="Link to this definition">#</a></dt>
<dd><p>Apply the power transform to each feature using the fitted lambdas.</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 of shape (n_samples, n_features)</span></dt><dd><p>The data to be transformed using a power transformation.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_trans</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>The transformed data.</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="Feature 0 (median income in a block) and feature 5 (average house occupancy) of the california_housing_dataset have very different scales and contain some very large outliers. These two characteristics lead to difficulties to visualize the data and, more importantly, they can degrade the predictive performance of many machine learning algorithms. Unscaled data can also slow down or even prevent the convergence of many gradient-based estimators."><img alt="" src="../../_images/sphx_glr_plot_all_scaling_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py"><span class="std std-ref">Compare the effect of different scalers on data with outliers</span></a></p>
<div class="sphx-glr-thumbnail-title">Compare the effect of different scalers on data with outliers</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms through PowerTransformer to map data from various distributions to a normal distribution."><img alt="" src="../../_images/sphx_glr_plot_map_data_to_normal_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/preprocessing/plot_map_data_to_normal.html#sphx-glr-auto-examples-preprocessing-plot-map-data-to-normal-py"><span class="std std-ref">Map data to a normal distribution</span></a></p>
<div class="sphx-glr-thumbnail-title">Map data to a normal distribution</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.preprocessing.PowerTransformer"><code class="docutils literal notranslate"><span class="pre">PowerTransformer</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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.inverse_transform"><code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.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.preprocessing.PowerTransformer.transform"><code class="docutils literal notranslate"><span class="pre">transform</span></code></a></li>
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