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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code>.TransformedTargetRegressor</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-compose-transformedtargetregressor">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.TransformedTargetRegressor</span></code></a></li>
</ul>
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<div class="section" id="sklearn-compose-transformedtargetregressor">
<h1><a class="reference internal" href="../classes.html#module-sklearn.compose" title="sklearn.compose"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.compose</span></code></a>.TransformedTargetRegressor<a class="headerlink" href="#sklearn-compose-transformedtargetregressor" title="Permalink to this headline">¶</a></h1>
<dl class="py class">
<dt id="sklearn.compose.TransformedTargetRegressor">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.compose.</code><code class="sig-name descname">TransformedTargetRegressor</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">regressor</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">transformer</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">func</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">inverse_func</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">check_inverse</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/compose/_target.py#L19"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor" title="Permalink to this definition">¶</a></dt>
<dd><p>Meta-estimator to regress on a transformed target.</p>
<p>Useful for applying a non-linear transformation to the target <code class="docutils literal notranslate"><span class="pre">y</span></code> in
regression problems. This transformation can be given as a Transformer
such as the QuantileTransformer or as a function and its inverse such as
<code class="docutils literal notranslate"><span class="pre">log</span></code> and <code class="docutils literal notranslate"><span class="pre">exp</span></code>.</p>
<p>The computation during <code class="docutils literal notranslate"><span class="pre">fit</span></code> is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">regressor</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">func</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
</pre></div>
</div>
<p>or:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">regressor</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">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
</pre></div>
</div>
<p>The computation during <code class="docutils literal notranslate"><span class="pre">predict</span></code> is:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">inverse_func</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
</pre></div>
</div>
<p>or:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">transformer</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
</pre></div>
</div>
<p>Read more in the <a class="reference internal" href="../compose.html#transformed-target-regressor"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>regressor</strong><span class="classifier">object, default=None</span></dt><dd><p>Regressor object such as derived from <code class="docutils literal notranslate"><span class="pre">RegressorMixin</span></code>. This
regressor will automatically be cloned each time prior to fitting.
If regressor is <code class="docutils literal notranslate"><span class="pre">None</span></code>, <code class="docutils literal notranslate"><span class="pre">LinearRegression()</span></code> is created and used.</p>
</dd>
<dt><strong>transformer</strong><span class="classifier">object, default=None</span></dt><dd><p>Estimator object such as derived from <code class="docutils literal notranslate"><span class="pre">TransformerMixin</span></code>. Cannot be
set at the same time as <code class="docutils literal notranslate"><span class="pre">func</span></code> and <code class="docutils literal notranslate"><span class="pre">inverse_func</span></code>. If
<code class="docutils literal notranslate"><span class="pre">transformer</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> as well as <code class="docutils literal notranslate"><span class="pre">func</span></code> and <code class="docutils literal notranslate"><span class="pre">inverse_func</span></code>,
the transformer will be an identity transformer. Note that the
transformer will be cloned during fitting. Also, the transformer is
restricting <code class="docutils literal notranslate"><span class="pre">y</span></code> to be a numpy array.</p>
</dd>
<dt><strong>func</strong><span class="classifier">function, default=None</span></dt><dd><p>Function to apply to <code class="docutils literal notranslate"><span class="pre">y</span></code> before passing to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. Cannot be set at
the same time as <code class="docutils literal notranslate"><span class="pre">transformer</span></code>. The function needs to return a
2-dimensional array. If <code class="docutils literal notranslate"><span class="pre">func</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, the function used will be
the identity function.</p>
</dd>
<dt><strong>inverse_func</strong><span class="classifier">function, default=None</span></dt><dd><p>Function to apply to the prediction of the regressor. Cannot be set at
the same time as <code class="docutils literal notranslate"><span class="pre">transformer</span></code> as well. The function needs to return
a 2-dimensional array. The inverse function is used to return
predictions to the same space of the original training labels.</p>
</dd>
<dt><strong>check_inverse</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to check that <code class="docutils literal notranslate"><span class="pre">transform</span></code> followed by <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>
or <code class="docutils literal notranslate"><span class="pre">func</span></code> followed by <code class="docutils literal notranslate"><span class="pre">inverse_func</span></code> leads to the original targets.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>regressor_</strong><span class="classifier">object</span></dt><dd><p>Fitted regressor.</p>
</dd>
<dt><strong>transformer_</strong><span class="classifier">object</span></dt><dd><p>Transformer used in <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>Internally, the target <code class="docutils literal notranslate"><span class="pre">y</span></code> is always converted into a 2-dimensional array
to be used by scikit-learn transformers. At the time of prediction, the
output will be reshaped to a have the same number of dimensions as <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
<p>See <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">examples/compose/plot_transformed_target.py</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="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.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">TransformedTargetRegressor</span>
<span class="gp">>>> </span><span class="n">tt</span> <span class="o">=</span> <span class="n">TransformedTargetRegressor</span><span class="p">(</span><span class="n">regressor</span><span class="o">=</span><span class="n">LinearRegression</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">func</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">,</span> <span class="n">inverse_func</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">exp</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">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">tt</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="go">TransformedTargetRegressor(...)</span>
<span class="gp">>>> </span><span class="n">tt</span><span class="o">.</span><span class="n">score</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="go">1.0</span>
<span class="gp">>>> </span><span class="n">tt</span><span class="o">.</span><span class="n">regressor_</span><span class="o">.</span><span class="n">coef_</span>
<span class="go">array([2.])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.TransformedTargetRegressor.fit" title="sklearn.compose.TransformedTargetRegressor.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y, **fit_params)</p></td>
<td><p>Fit the model according to the given training data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.TransformedTargetRegressor.get_params" title="sklearn.compose.TransformedTargetRegressor.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.compose.TransformedTargetRegressor.predict" title="sklearn.compose.TransformedTargetRegressor.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X)</p></td>
<td><p>Predict using the base regressor, applying inverse.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.compose.TransformedTargetRegressor.score" title="sklearn.compose.TransformedTargetRegressor.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>Return the coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> of the prediction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.compose.TransformedTargetRegressor.set_params" title="sklearn.compose.TransformedTargetRegressor.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>
</tbody>
</table>
<dl class="py method">
<dt id="sklearn.compose.TransformedTargetRegressor.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">fit_params</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/compose/_target.py#L157"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the model according to the given training data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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>Training vector, where n_samples is the number of samples and
n_features is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Parameters passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method of the underlying
regressor.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.compose.TransformedTargetRegressor.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">deep</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L178"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.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</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</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 id="sklearn.compose.TransformedTargetRegressor.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/compose/_target.py#L211"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict using the base regressor, applying inverse.</p>
<p>The regressor is used to predict and the <code class="docutils literal notranslate"><span class="pre">inverse_func</span></code> or
<code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> is applied before returning the prediction.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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>Samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_hat</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Predicted values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.compose.TransformedTargetRegressor.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span></em>, <em class="sig-param"><span class="n">sample_weight</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L510"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> of the
prediction.</p>
<p>The coefficient <span class="math notranslate nohighlight">\(R^2\)</span> is defined as <span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>,
where <span class="math notranslate nohighlight">\(u\)</span> is the residual sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)</span>
<span class="pre">**</span> <span class="pre">2).sum()</span></code> and <span class="math notranslate nohighlight">\(v\)</span> is the total sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span>
<span class="pre">y_true.mean())</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code>. The best possible score is 1.0 and it
can be negative (because the model can be arbitrarily worse). A
constant model that always predicts the expected value of <code class="docutils literal notranslate"><span class="pre">y</span></code>,
disregarding the input features, would get a <span class="math notranslate nohighlight">\(R^2\)</span> score of
0.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</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>Test samples. For some estimators this may be a precomputed
kernel matrix or a list of generic objects instead with shape
<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_samples_fitted)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n_samples_fitted</span></code>
is the number of samples used in the fitting for the estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p><span class="math notranslate nohighlight">\(R^2\)</span> of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> wrt. <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The <span class="math notranslate nohighlight">\(R^2\)</span> score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor uses
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
with default value of <a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a>.
This influences the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput
regressors (except for
<a class="reference internal" href="sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>).</p>
</dd></dl>
<dl class="py method">
<dt id="sklearn.compose.TransformedTargetRegressor.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L202"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.compose.TransformedTargetRegressor.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</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</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>
<div class="section" id="examples-using-sklearn-compose-transformedtargetregressor">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.compose.TransformedTargetRegressor</span></code><a class="headerlink" href="#examples-using-sklearn-compose-transformedtargetregressor" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of log-linear Poisson regression on the `French Motor Third-Pa..."><div class="figure align-default" id="id1">
<img alt="Poisson regression and non-normal loss" src="../../_images/sphx_glr_plot_poisson_regression_non_normal_loss_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html#sphx-glr-auto-examples-linear-model-plot-poisson-regression-non-normal-loss-py"><span class="std std-ref">Poisson regression and non-normal loss</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In linear models, the target value is modeled as a linear combination of the features (see the ..."><div class="figure align-default" id="id2">
<img alt="Common pitfalls in interpretation of coefficients of linear models" src="../../_images/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in interpretation of coefficients of linear models</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we give an overview of TransformedTargetRegressor. We use two examples to illu..."><div class="figure align-default" id="id3">
<img alt="Effect of transforming the targets in regression model" src="../../_images/sphx_glr_plot_transformed_target_thumb.png" />
<p class="caption"><span class="caption-text"><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></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
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