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<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../inspection.html">4. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="permutation_importance.html">4.2. Permutation feature importance</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../visualizations.html">5. Visualizations</a></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../data_transforms.html">6. Dataset transformations</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2 current active"><a class="current reference internal" href="#">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="pipelines-and-composite-estimators">
<span id="combining-estimators"></span><h1><span class="section-number">6.1. </span>Pipelines and composite estimators<a class="headerlink" href="#pipelines-and-composite-estimators" title="Link to this heading">#</a></h1>
<p>To build a composite estimator, transformers are usually combined with other
transformers or with <a class="reference internal" href="../glossary.html#term-predictors"><span class="xref std std-term">predictors</span></a> (such as classifiers or regressors).
The most common tool used for composing estimators is a <a class="reference internal" href="#pipeline"><span class="std std-ref">Pipeline</span></a>. Pipelines require all steps except the last to be a
<a class="reference internal" href="../glossary.html#term-transformer"><span class="xref std std-term">transformer</span></a>. The last step can be anything, a transformer, a
<a class="reference internal" href="../glossary.html#term-predictor"><span class="xref std std-term">predictor</span></a>, or a clustering estimator which might have or not have a
<code class="docutils literal notranslate"><span class="pre">.predict(...)</span></code> method. A pipeline exposes all methods provided by the last
estimator: if the last step provides a <code class="docutils literal notranslate"><span class="pre">transform</span></code> method, then the pipeline
would have a <code class="docutils literal notranslate"><span class="pre">transform</span></code> method and behave like a transformer. If the last step
provides a <code class="docutils literal notranslate"><span class="pre">predict</span></code> method, then the pipeline would expose that method, and
given a data <a class="reference internal" href="../glossary.html#term-X"><span class="xref std std-term">X</span></a>, use all steps except the last to transform the data,
and then give that transformed data to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method of the last step of
the pipeline. The class <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code> is often used in combination with
<a class="reference internal" href="#column-transformer"><span class="std std-ref">ColumnTransformer</span></a> or
<a class="reference internal" href="#feature-union"><span class="std std-ref">FeatureUnion</span></a> which concatenate the output of transformers
into a composite feature space.
<a class="reference internal" href="#transformed-target-regressor"><span class="std std-ref">TransformedTargetRegressor</span></a>
deals with transforming the <a class="reference internal" href="../glossary.html#term-target"><span class="xref std std-term">target</span></a> (i.e. log-transform <a class="reference internal" href="../glossary.html#term-y"><span class="xref std std-term">y</span></a>).</p>
<section id="pipeline-chaining-estimators">
<span id="pipeline"></span><h2><span class="section-number">6.1.1. </span>Pipeline: chaining estimators<a class="headerlink" href="#pipeline-chaining-estimators" title="Link to this heading">#</a></h2>
<p><a class="reference internal" href="generated/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> can be used to chain multiple estimators
into one. This is useful as there is often a fixed sequence
of steps in processing the data, for example feature selection, normalization
and classification. <a class="reference internal" href="generated/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> serves multiple purposes here:</p>
<dl class="simple">
<dt>Convenience and encapsulation</dt><dd><p>You only have to call <a class="reference internal" href="../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> and <a class="reference internal" href="../glossary.html#term-predict"><span class="xref std std-term">predict</span></a> once on your
data to fit a whole sequence of estimators.</p>
</dd>
<dt>Joint parameter selection</dt><dd><p>You can <a class="reference internal" href="grid_search.html#grid-search"><span class="std std-ref">grid search</span></a>
over parameters of all estimators in the pipeline at once.</p>
</dd>
<dt>Safety</dt><dd><p>Pipelines help avoid leaking statistics from your test data into the
trained model in cross-validation, by ensuring that the same samples are
used to train the transformers and predictors.</p>
</dd>
</dl>
<p>All estimators in a pipeline, except the last one, must be transformers
(i.e. must have a <a class="reference internal" href="../glossary.html#term-transform"><span class="xref std std-term">transform</span></a> method).
The last estimator may be any type (transformer, classifier, etc.).</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Calling <code class="docutils literal notranslate"><span class="pre">fit</span></code> on the pipeline is the same as calling <code class="docutils literal notranslate"><span class="pre">fit</span></code> on
each estimator in turn, <code class="docutils literal notranslate"><span class="pre">transform</span></code> the input and pass it on to the next step.
The pipeline has all the methods that the last estimator in the pipeline has,
i.e. if the last estimator is a classifier, the <a class="reference internal" href="generated/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> can be used
as a classifier. If the last estimator is a transformer, again, so is the
pipeline.</p>
</div>
<section id="usage">
<h3><span class="section-number">6.1.1.1. </span>Usage<a class="headerlink" href="#usage" title="Link to this heading">#</a></h3>
<section id="build-a-pipeline">
<h4><span class="section-number">6.1.1.1.1. </span>Build a pipeline<a class="headerlink" href="#build-a-pipeline" title="Link to this heading">#</a></h4>
<p>The <a class="reference internal" href="generated/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> is built using a list of <code class="docutils literal notranslate"><span class="pre">(key,</span> <span class="pre">value)</span></code> pairs, where
the <code class="docutils literal notranslate"><span class="pre">key</span></code> is a string containing the name you want to give this step and <code class="docutils literal notranslate"><span class="pre">value</span></code>
is an estimator object:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">Pipeline</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">PCA</span>
<span class="gp">>>> </span><span class="n">estimators</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">'reduce_dim'</span><span class="p">,</span> <span class="n">PCA</span><span class="p">()),</span> <span class="p">(</span><span class="s1">'clf'</span><span class="p">,</span> <span class="n">SVC</span><span class="p">())]</span>
<span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">estimators</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pipe</span>
<span class="go">Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())])</span>
</pre></div>
</div>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="shorthand-version-using-make_pipeline">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Shorthand version using <a class="reference internal" href="generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_pipeline</span></code></a><a class="headerlink" href="#shorthand-version-using-make_pipeline" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">The utility function <a class="reference internal" href="generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_pipeline</span></code></a> is a shorthand
for constructing pipelines;
it takes a variable number of estimators and returns a pipeline,
filling in the names automatically:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">>>> </span><span class="n">make_pipeline</span><span class="p">(</span><span class="n">PCA</span><span class="p">(),</span> <span class="n">SVC</span><span class="p">())</span>
<span class="go">Pipeline(steps=[('pca', PCA()), ('svc', SVC())])</span>
</pre></div>
</div>
</div>
</details></section>
<section id="access-pipeline-steps">
<h4><span class="section-number">6.1.1.1.2. </span>Access pipeline steps<a class="headerlink" href="#access-pipeline-steps" title="Link to this heading">#</a></h4>
<p>The estimators of a pipeline are stored as a list in the <code class="docutils literal notranslate"><span class="pre">steps</span></code> attribute.
A sub-pipeline can be extracted using the slicing notation commonly used
for Python Sequences such as lists or strings (although only a step of 1 is
permitted). This is convenient for performing only some of the transformations
(or their inverse):</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pipe</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span>
<span class="go">Pipeline(steps=[('reduce_dim', PCA())])</span>
<span class="gp">>>> </span><span class="n">pipe</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">:]</span>
<span class="go">Pipeline(steps=[('clf', SVC())])</span>
</pre></div>
</div>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="accessing-a-step-by-name-or-position">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Accessing a step by name or position<a class="headerlink" href="#accessing-a-step-by-name-or-position" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">A specific step can also be accessed by index or name by indexing (with <code class="docutils literal notranslate"><span class="pre">[idx]</span></code>) the
pipeline:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pipe</span><span class="o">.</span><span class="n">steps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="go">('reduce_dim', PCA())</span>
<span class="gp">>>> </span><span class="n">pipe</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="go">PCA()</span>
<span class="gp">>>> </span><span class="n">pipe</span><span class="p">[</span><span class="s1">'reduce_dim'</span><span class="p">]</span>
<span class="go">PCA()</span>
</pre></div>
</div>
<p class="sd-card-text"><code class="docutils literal notranslate"><span class="pre">Pipeline</span></code>’s <code class="docutils literal notranslate"><span class="pre">named_steps</span></code> attribute allows accessing steps by name with tab
completion in interactive environments:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pipe</span><span class="o">.</span><span class="n">named_steps</span><span class="o">.</span><span class="n">reduce_dim</span> <span class="ow">is</span> <span class="n">pipe</span><span class="p">[</span><span class="s1">'reduce_dim'</span><span class="p">]</span>
<span class="go">True</span>
</pre></div>
</div>
</div>
</details></section>
<section id="tracking-feature-names-in-a-pipeline">
<h4><span class="section-number">6.1.1.1.3. </span>Tracking feature names in a pipeline<a class="headerlink" href="#tracking-feature-names-in-a-pipeline" title="Link to this heading">#</a></h4>
<p>To enable model inspection, <a class="reference internal" href="generated/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> has a
<code class="docutils literal notranslate"><span class="pre">get_feature_names_out()</span></code> method, just like all transformers. You can use
pipeline slicing to get the feature names going into each step:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">SelectKBest</span>
<span class="gp">>>> </span><span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">steps</span><span class="o">=</span><span class="p">[</span>
<span class="gp">... </span> <span class="p">(</span><span class="s1">'select'</span><span class="p">,</span> <span class="n">SelectKBest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)),</span>
<span class="gp">... </span> <span class="p">(</span><span class="s1">'clf'</span><span class="p">,</span> <span class="n">LogisticRegression</span><span class="p">())])</span>
<span class="gp">>>> </span><span class="n">pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="go">Pipeline(steps=[('select', SelectKBest(...)), ('clf', LogisticRegression(...))])</span>
<span class="gp">>>> </span><span class="n">pipe</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">()</span>
<span class="go">array(['x2', 'x3'], ...)</span>
</pre></div>
</div>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="customize-feature-names">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Customize feature names<a class="headerlink" href="#customize-feature-names" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">You can also provide custom feature names for the input data using
<code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pipe</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)</span>
<span class="go">array(['petal length (cm)', 'petal width (cm)'], ...)</span>
</pre></div>
</div>
</div>
</details></section>
<section id="access-to-nested-parameters">
<span id="pipeline-nested-parameters"></span><h4><span class="section-number">6.1.1.1.4. </span>Access to nested parameters<a class="headerlink" href="#access-to-nested-parameters" title="Link to this heading">#</a></h4>
<p>It is common to adjust the parameters of an estimator within a pipeline. This parameter
is therefore nested because it belongs to a particular sub-step. Parameters of the
estimators in the pipeline are accessible using the <code class="docutils literal notranslate"><span class="pre"><estimator>__<parameter></span></code>
syntax:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">steps</span><span class="o">=</span><span class="p">[(</span><span class="s2">"reduce_dim"</span><span class="p">,</span> <span class="n">PCA</span><span class="p">()),</span> <span class="p">(</span><span class="s2">"clf"</span><span class="p">,</span> <span class="n">SVC</span><span class="p">())])</span>
<span class="gp">>>> </span><span class="n">pipe</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">clf__C</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="go">Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))])</span>
</pre></div>
</div>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="when-does-it-matter?">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">When does it matter?<a class="headerlink" href="#when-does-it-matter?" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">This is particularly important for doing grid searches:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">reduce_dim__n_components</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
<span class="gp">... </span> <span class="n">clf__C</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">pipe</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">)</span>
</pre></div>
</div>
<p class="sd-card-text">Individual steps may also be replaced as parameters, and non-final steps may be
ignored by setting them to <code class="docutils literal notranslate"><span class="pre">'passthrough'</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">reduce_dim</span><span class="o">=</span><span class="p">[</span><span class="s1">'passthrough'</span><span class="p">,</span> <span class="n">PCA</span><span class="p">(</span><span class="mi">5</span><span class="p">),</span> <span class="n">PCA</span><span class="p">(</span><span class="mi">10</span><span class="p">)],</span>
<span class="gp">... </span> <span class="n">clf</span><span class="o">=</span><span class="p">[</span><span class="n">SVC</span><span class="p">(),</span> <span class="n">LogisticRegression</span><span class="p">()],</span>
<span class="gp">... </span> <span class="n">clf__C</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">pipe</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<ul class="simple">
<li><p class="sd-card-text"><a class="reference internal" href="grid_search.html#composite-grid-search"><span class="std std-ref">Composite estimators and parameter spaces</span></a></p></li>
</ul>
</div>
</div>
</details><p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py"><span class="std std-ref">Pipeline ANOVA SVM</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/model_selection/plot_grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-plot-grid-search-text-feature-extraction-py"><span class="std std-ref">Sample pipeline for text feature extraction and evaluation</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/compose/plot_digits_pipe.html#sphx-glr-auto-examples-compose-plot-digits-pipe-py"><span class="std std-ref">Pipelining: chaining a PCA and a logistic regression</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/miscellaneous/plot_kernel_approximation.html#sphx-glr-auto-examples-miscellaneous-plot-kernel-approximation-py"><span class="std std-ref">Explicit feature map approximation for RBF kernels</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/svm/plot_svm_anova.html#sphx-glr-auto-examples-svm-plot-svm-anova-py"><span class="std std-ref">SVM-Anova: SVM with univariate feature selection</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py"><span class="std std-ref">Selecting dimensionality reduction with Pipeline and GridSearchCV</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/miscellaneous/plot_pipeline_display.html#sphx-glr-auto-examples-miscellaneous-plot-pipeline-display-py"><span class="std std-ref">Displaying Pipelines</span></a></p></li>
</ul>
</section>
</section>
<section id="caching-transformers-avoid-repeated-computation">
<span id="pipeline-cache"></span><h3><span class="section-number">6.1.1.2. </span>Caching transformers: avoid repeated computation<a class="headerlink" href="#caching-transformers-avoid-repeated-computation" title="Link to this heading">#</a></h3>
<p>Fitting transformers may be computationally expensive. With its
<code class="docutils literal notranslate"><span class="pre">memory</span></code> parameter set, <a class="reference internal" href="generated/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> will cache each transformer
after calling <code class="docutils literal notranslate"><span class="pre">fit</span></code>.
This feature is used to avoid computing the fit transformers within a pipeline
if the parameters and input data are identical. A typical example is the case of
a grid search in which the transformers can be fitted only once and reused for
each configuration. The last step will never be cached, even if it is a transformer.</p>
<p>The parameter <code class="docutils literal notranslate"><span class="pre">memory</span></code> is needed in order to cache the transformers.
<code class="docutils literal notranslate"><span class="pre">memory</span></code> can be either a string containing the directory where to cache the
transformers or a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/memory.html">joblib.Memory</a>
object:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">tempfile</span><span class="w"> </span><span class="kn">import</span> <span class="n">mkdtemp</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">shutil</span><span class="w"> </span><span class="kn">import</span> <span class="n">rmtree</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">PCA</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">Pipeline</span>
<span class="gp">>>> </span><span class="n">estimators</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">'reduce_dim'</span><span class="p">,</span> <span class="n">PCA</span><span class="p">()),</span> <span class="p">(</span><span class="s1">'clf'</span><span class="p">,</span> <span class="n">SVC</span><span class="p">())]</span>
<span class="gp">>>> </span><span class="n">cachedir</span> <span class="o">=</span> <span class="n">mkdtemp</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">estimators</span><span class="p">,</span> <span class="n">memory</span><span class="o">=</span><span class="n">cachedir</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pipe</span>
<span class="go">Pipeline(memory=...,</span>
<span class="go"> steps=[('reduce_dim', PCA()), ('clf', SVC())])</span>
<span class="gp">>>> </span><span class="c1"># Clear the cache directory when you don't need it anymore</span>
<span class="gp">>>> </span><span class="n">rmtree</span><span class="p">(</span><span class="n">cachedir</span><span class="p">)</span>
</pre></div>
</div>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="side-effect-of-caching-transformers">
<summary class="sd-summary-title sd-card-header sd-bg-warning sd-bg-text-warning">
<span class="sd-summary-text">Side effect of caching transformers<a class="headerlink" href="#side-effect-of-caching-transformers" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">Using a <a class="reference internal" href="generated/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> without cache enabled, it is possible to
inspect the original instance such as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_digits</span>
<span class="gp">>>> </span><span class="n">X_digits</span><span class="p">,</span> <span class="n">y_digits</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pca1</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">svm1</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([(</span><span class="s1">'reduce_dim'</span><span class="p">,</span> <span class="n">pca1</span><span class="p">),</span> <span class="p">(</span><span class="s1">'clf'</span><span class="p">,</span> <span class="n">svm1</span><span class="p">)])</span>
<span class="gp">>>> </span><span class="n">pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_digits</span><span class="p">,</span> <span class="n">y_digits</span><span class="p">)</span>
<span class="go">Pipeline(steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())])</span>
<span class="gp">>>> </span><span class="c1"># The pca instance can be inspected directly</span>
<span class="gp">>>> </span><span class="n">pca1</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(10, 64)</span>
</pre></div>
</div>
<p class="sd-card-text">Enabling caching triggers a clone of the transformers before fitting.
Therefore, the transformer instance given to the pipeline cannot be
inspected directly.
In the following example, accessing the <a class="reference internal" href="generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">PCA</span></code></a>
instance <code class="docutils literal notranslate"><span class="pre">pca2</span></code> will raise an <code class="docutils literal notranslate"><span class="pre">AttributeError</span></code> since <code class="docutils literal notranslate"><span class="pre">pca2</span></code> will be an
unfitted transformer.
Instead, use the attribute <code class="docutils literal notranslate"><span class="pre">named_steps</span></code> to inspect estimators within
the pipeline:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">cachedir</span> <span class="o">=</span> <span class="n">mkdtemp</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">pca2</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">svm2</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">cached_pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([(</span><span class="s1">'reduce_dim'</span><span class="p">,</span> <span class="n">pca2</span><span class="p">),</span> <span class="p">(</span><span class="s1">'clf'</span><span class="p">,</span> <span class="n">svm2</span><span class="p">)],</span>
<span class="gp">... </span> <span class="n">memory</span><span class="o">=</span><span class="n">cachedir</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cached_pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_digits</span><span class="p">,</span> <span class="n">y_digits</span><span class="p">)</span>
<span class="go">Pipeline(memory=...,</span>
<span class="go"> steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())])</span>
<span class="gp">>>> </span><span class="n">cached_pipe</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">'reduce_dim'</span><span class="p">]</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(10, 64)</span>
<span class="gp">>>> </span><span class="c1"># Remove the cache directory</span>
<span class="gp">>>> </span><span class="n">rmtree</span><span class="p">(</span><span class="n">cachedir</span><span class="p">)</span>
</pre></div>
</div>
</div>
</details><p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py"><span class="std std-ref">Selecting dimensionality reduction with Pipeline and GridSearchCV</span></a></p></li>
</ul>
</section>
</section>
<section id="transforming-target-in-regression">
<span id="transformed-target-regressor"></span><h2><span class="section-number">6.1.2. </span>Transforming target in regression<a class="headerlink" href="#transforming-target-in-regression" title="Link to this heading">#</a></h2>
<p><a class="reference internal" href="generated/sklearn.compose.TransformedTargetRegressor.html#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose.TransformedTargetRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedTargetRegressor</span></code></a> transforms the
targets <code class="docutils literal notranslate"><span class="pre">y</span></code> before fitting a regression model. The predictions are mapped
back to the original space via an inverse transform. It takes as an argument
the regressor that will be used for prediction, and the transformer that will
be applied to the target variable:</p>
<div class="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.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_california_housing</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <span class="n">TransformedTargetRegressor</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">QuantileTransformer</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_california_housing</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="mi">2000</span><span class="p">,</span> <span class="p">:],</span> <span class="n">y</span><span class="p">[:</span><span class="mi">2000</span><span class="p">]</span> <span class="c1"># select a subset of data</span>
<span class="gp">>>> </span><span class="n">transformer</span> <span class="o">=</span> <span class="n">QuantileTransformer</span><span class="p">(</span><span class="n">output_distribution</span><span class="o">=</span><span class="s1">'normal'</span><span class="p">)</span>
<span class="gp">>>> </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">regr</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">regressor</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">transformer</span><span class="o">=</span><span class="n">transformer</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</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="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">regr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">TransformedTargetRegressor(...)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="s1">'R2 score: </span><span class="si">{0:.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)))</span>
<span class="go">R2 score: 0.61</span>
<span class="gp">>>> </span><span class="n">raw_target_regr</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="s1">'R2 score: </span><span class="si">{0:.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">raw_target_regr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)))</span>
<span class="go">R2 score: 0.59</span>
</pre></div>
</div>
<p>For simple transformations, instead of a Transformer object, a pair of
functions can be passed, defining the transformation and its inverse mapping:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span><span class="w"> </span><span class="nf">func</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">log</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">def</span><span class="w"> </span><span class="nf">inverse_func</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">exp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>Subsequently, the object is created as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">regr</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">regressor</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">func</span><span class="o">=</span><span class="n">func</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">inverse_func</span><span class="o">=</span><span class="n">inverse_func</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">regr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">TransformedTargetRegressor(...)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="s1">'R2 score: </span><span class="si">{0:.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">regr</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)))</span>
<span class="go">R2 score: 0.51</span>
</pre></div>