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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="modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/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="modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="model_selection.html">3. Model selection and evaluation</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="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/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="modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/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="current reference internal" href="#">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>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/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="modules/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="dataset-transformations">
<span id="data-transforms"></span><h1><span class="section-number">6. </span>Dataset transformations<a class="headerlink" href="#dataset-transformations" title="Link to this heading">#</a></h1>
<p>scikit-learn provides a library of transformers, which may clean (see
<a class="reference internal" href="modules/preprocessing.html#preprocessing"><span class="std std-ref">Preprocessing data</span></a>), reduce (see <a class="reference internal" href="modules/unsupervised_reduction.html#data-reduction"><span class="std std-ref">Unsupervised dimensionality reduction</span></a>), expand (see
<a class="reference internal" href="modules/kernel_approximation.html#kernel-approximation"><span class="std std-ref">Kernel Approximation</span></a>) or generate (see <a class="reference internal" href="modules/feature_extraction.html#feature-extraction"><span class="std std-ref">Feature extraction</span></a>)
feature representations.</p>
<p>Like other estimators, these are represented by classes with a <code class="docutils literal notranslate"><span class="pre">fit</span></code> method,
which learns model parameters (e.g. mean and standard deviation for
normalization) from a training set, and a <code class="docutils literal notranslate"><span class="pre">transform</span></code> method which applies
this transformation model to unseen data. <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code> may be more
convenient and efficient for modelling and transforming the training data
simultaneously.</p>
<p>Combining such transformers, either in parallel or series is covered in
<a class="reference internal" href="modules/compose.html#combining-estimators"><span class="std std-ref">Pipelines and composite estimators</span></a>. <a class="reference internal" href="modules/metrics.html#metrics"><span class="std std-ref">Pairwise metrics, Affinities and Kernels</span></a> covers transforming feature
spaces into affinity matrices, while <a class="reference internal" href="modules/preprocessing_targets.html#preprocessing-targets"><span class="std std-ref">Transforming the prediction target (y)</span></a> considers
transformations of the target space (e.g. categorical labels) for use in
scikit-learn.</p>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#pipeline-chaining-estimators">6.1.1. Pipeline: chaining estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#transforming-target-in-regression">6.1.2. Transforming target in regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#featureunion-composite-feature-spaces">6.1.3. FeatureUnion: composite feature spaces</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#columntransformer-for-heterogeneous-data">6.1.4. ColumnTransformer for heterogeneous data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html#visualizing-composite-estimators">6.1.5. Visualizing Composite Estimators</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/feature_extraction.html">6.2. Feature extraction</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#loading-features-from-dicts">6.2.1. Loading features from dicts</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#feature-hashing">6.2.2. Feature hashing</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#text-feature-extraction">6.2.3. Text feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html#image-feature-extraction">6.2.4. Image feature extraction</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/preprocessing.html">6.3. Preprocessing data</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling">6.3.1. Standardization, or mean removal and variance scaling</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#non-linear-transformation">6.3.2. Non-linear transformation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#normalization">6.3.3. Normalization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#encoding-categorical-features">6.3.4. Encoding categorical features</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#discretization">6.3.5. Discretization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#imputation-of-missing-values">6.3.6. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#generating-polynomial-features">6.3.7. Generating polynomial features</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html#custom-transformers">6.3.8. Custom transformers</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/impute.html">6.4. Imputation of missing values</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#univariate-vs-multivariate-imputation">6.4.1. Univariate vs. Multivariate Imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#univariate-feature-imputation">6.4.2. Univariate feature imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#multivariate-feature-imputation">6.4.3. Multivariate feature imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#nearest-neighbors-imputation">6.4.4. Nearest neighbors imputation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#keeping-the-number-of-features-constant">6.4.5. Keeping the number of features constant</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#marking-imputed-values">6.4.6. Marking imputed values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html#estimators-that-handle-nan-values">6.4.7. Estimators that handle NaN values</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html#pca-principal-component-analysis">6.5.1. PCA: principal component analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html#random-projections">6.5.2. Random projections</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html#feature-agglomeration">6.5.3. Feature agglomeration</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/random_projection.html">6.6. Random Projection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#the-johnson-lindenstrauss-lemma">6.6.1. The Johnson-Lindenstrauss lemma</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#gaussian-random-projection">6.6.2. Gaussian random projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#sparse-random-projection">6.6.3. Sparse random projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html#inverse-transform">6.6.4. Inverse Transform</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/kernel_approximation.html">6.7. Kernel Approximation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#nystroem-method-for-kernel-approximation">6.7.1. Nystroem Method for Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#radial-basis-function-kernel">6.7.2. Radial Basis Function Kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#additive-chi-squared-kernel">6.7.3. Additive Chi Squared Kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#skewed-chi-squared-kernel">6.7.4. Skewed Chi Squared Kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#polynomial-kernel-approximation-via-tensor-sketch">6.7.5. Polynomial Kernel Approximation via Tensor Sketch</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html#mathematical-details">6.7.6. Mathematical Details</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#cosine-similarity">6.8.1. Cosine similarity</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#linear-kernel">6.8.2. Linear kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#polynomial-kernel">6.8.3. Polynomial kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#sigmoid-kernel">6.8.4. Sigmoid kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#rbf-kernel">6.8.5. RBF kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#laplacian-kernel">6.8.6. Laplacian kernel</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html#chi-squared-kernel">6.8.7. Chi-squared kernel</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html#label-binarization">6.9.1. Label binarization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html#label-encoding">6.9.2. Label encoding</a></li>
</ul>
</li>
</ul>
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