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<section id="quantile-transform">
<h1>quantile_transform<a class="headerlink" href="#quantile-transform" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.preprocessing.quantile_transform">
<span class="sig-prename descclassname"><span class="pre">sklearn.preprocessing.</span></span><span class="sig-name descname"><span class="pre">quantile_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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</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">n_quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_distribution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_implicit_zeros</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100000</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">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/86d099ec1/sklearn/preprocessing/_data.py#L3028"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.preprocessing.quantile_transform" title="Link to this definition">#</a></dt>
<dd><p>Transform features using quantiles information.</p>
<p>This method transforms the features to follow a uniform or a normal
distribution. Therefore, for a given feature, this transformation tends
to spread out the most frequent values. It also reduces the impact of
(marginal) outliers: this is therefore a robust preprocessing scheme.</p>
<p>The transformation is applied on each feature independently. First an
estimate of the cumulative distribution function of a feature is
used to map the original values to a uniform distribution. The obtained
values are then mapped to the desired output distribution using the
associated quantile function. Features values of new/unseen data that fall
below or above the fitted range will be mapped to the bounds of the output
distribution. Note that this transform is non-linear. It may distort linear
correlations between variables measured at the same scale but renders
variables measured at different scales more directly comparable.</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>
<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>The data to transform.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">int, default=0</span></dt><dd><p>Axis used to compute the means and standard deviations along. If 0,
transform each feature, otherwise (if 1) transform each sample.</p>
</dd>
<dt><strong>n_quantiles</strong><span class="classifier">int, default=1000 or n_samples</span></dt><dd><p>Number of quantiles to be computed. It corresponds to the number
of landmarks used to discretize the cumulative distribution function.
If n_quantiles is larger than the number of samples, n_quantiles is set
to the number of samples as a larger number of quantiles does not give
a better approximation of the cumulative distribution function
estimator.</p>
</dd>
<dt><strong>output_distribution</strong><span class="classifier">{‘uniform’, ‘normal’}, default=’uniform’</span></dt><dd><p>Marginal distribution for the transformed data. The choices are
‘uniform’ (default) or ‘normal’.</p>
</dd>
<dt><strong>ignore_implicit_zeros</strong><span class="classifier">bool, default=False</span></dt><dd><p>Only applies to sparse matrices. If True, the sparse entries of the
matrix are discarded to compute the quantile statistics. If False,
these entries are treated as zeros.</p>
</dd>
<dt><strong>subsample</strong><span class="classifier">int or None, default=1e5</span></dt><dd><p>Maximum number of samples used to estimate the quantiles for
computational efficiency. Note that the subsampling procedure may
differ for value-identical sparse and dense matrices.
Disable subsampling by setting <code class="docutils literal notranslate"><span class="pre">subsample=None</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.5: </span>The option <code class="docutils literal notranslate"><span class="pre">None</span></code> to disable subsampling was added.</p>
</div>
</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 subsampling and smoothing
noise.
Please see <code class="docutils literal notranslate"><span class="pre">subsample</span></code> for more details.
Pass an int for reproducible results across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, default=True</span></dt><dd><p>If False, try to avoid a copy and transform in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.23: </span>The default value of <code class="docutils literal notranslate"><span class="pre">copy</span></code> changed from False to True in 0.23.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Xt</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The transformed data.</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.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>Performs quantile-based scaling using the Transformer API (e.g. as part of a preprocessing <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>).</p>
</dd>
<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>Maps data to a normal distribution using a power transformation.</p>
</dd>
<dt><a class="reference internal" href="sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scale</span></code></a></dt><dd><p>Performs standardization that is faster, but less robust to outliers.</p>
</dd>
<dt><a class="reference internal" href="sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">robust_scale</span></code></a></dt><dd><p>Performs robust standardization that removes the influence of outliers but does not put outliers and inliers on the same scale.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>NaNs are treated as missing values: disregarded in fit, and maintained in
transform.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Risk of data leak</p>
<p>Do not use <a class="reference internal" href="#sklearn.preprocessing.quantile_transform" title="sklearn.preprocessing.quantile_transform"><code class="xref py py-func docutils literal notranslate"><span class="pre">quantile_transform</span></code></a> unless
you know what you are doing. A common mistake is to apply it
to the entire data <em>before</em> splitting into training and
test sets. This will bias the model evaluation because
information would have leaked from the test set to the
training set.
In general, we recommend using
<a class="reference internal" href="sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer" title="sklearn.preprocessing.QuantileTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantileTransformer</span></code></a> within a
<a class="reference internal" href="../compose.html#pipeline"><span class="std std-ref">Pipeline</span></a> in order to prevent most risks of data
leaking:<code class="docutils literal notranslate"><span class="pre">pipe</span> <span class="pre">=</span> <span class="pre">make_pipeline(QuantileTransformer(),</span>
<span class="pre">LogisticRegression())</span></code>.</p>
</div>
<p>For a comparison of the different scalers, transformers, and normalizers,
see: <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>
<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="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">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">quantile_transform</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</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">sort</span><span class="p">(</span><span class="n">rng</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">quantile_transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">n_quantiles</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">array([...])</span>
</pre></div>
</div>
</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="In this example, we give an overview of TransformedTargetRegressor. We use two examples to illustrate the benefit of transforming the targets before learning a linear regression model. The first example uses synthetic data while the second example is based on the Ames housing data set."><img alt="" src="../../_images/sphx_glr_plot_transformed_target_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/compose/plot_transformed_target.html#sphx-glr-auto-examples-compose-plot-transformed-target-py"><span class="std std-ref">Effect of transforming the targets in regression model</span></a></p>
<div class="sphx-glr-thumbnail-title">Effect of transforming the targets in regression model</div>
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