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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>
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<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>
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<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>
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<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">2. Unsupervised learning</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/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>
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<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>
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<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>
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<li class="toctree-l1 has-children"><a class="reference internal" href="data_transforms.html">6. Dataset transformations</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/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>
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<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>
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<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>
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<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">11.1. Array API support (experimental)</a></li>
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<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="unsupervised-learning">
<span id="id1"></span><h1><span class="section-number">2. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Link to this heading">#</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html#gaussian-mixture">2.1.1. Gaussian Mixture</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html#variational-bayesian-gaussian-mixture">2.1.2. Variational Bayesian Gaussian Mixture</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#introduction">2.2.1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#isomap">2.2.2. Isomap</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#locally-linear-embedding">2.2.3. Locally Linear Embedding</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#modified-locally-linear-embedding">2.2.4. Modified Locally Linear Embedding</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#hessian-eigenmapping">2.2.5. Hessian Eigenmapping</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#spectral-embedding">2.2.6. Spectral Embedding</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#local-tangent-space-alignment">2.2.7. Local Tangent Space Alignment</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#multi-dimensional-scaling-mds">2.2.8. Multi-dimensional Scaling (MDS)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#t-distributed-stochastic-neighbor-embedding-t-sne">2.2.9. t-distributed Stochastic Neighbor Embedding (t-SNE)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html#tips-on-practical-use">2.2.10. Tips on practical use</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#overview-of-clustering-methods">2.3.1. Overview of clustering methods</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#k-means">2.3.2. K-means</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#affinity-propagation">2.3.3. Affinity Propagation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#mean-shift">2.3.4. Mean Shift</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#spectral-clustering">2.3.5. Spectral clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#hierarchical-clustering">2.3.6. Hierarchical clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#dbscan">2.3.7. DBSCAN</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#hdbscan">2.3.8. HDBSCAN</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#optics">2.3.9. OPTICS</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#birch">2.3.10. BIRCH</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#clustering-performance-evaluation">2.3.11. Clustering performance evaluation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html#spectral-co-clustering">2.4.1. Spectral Co-Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html#spectral-biclustering">2.4.2. Spectral Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html#biclustering-evaluation">2.4.3. Biclustering evaluation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#principal-component-analysis-pca">2.5.1. Principal component analysis (PCA)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#kernel-principal-component-analysis-kpca">2.5.2. Kernel Principal Component Analysis (kPCA)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#truncated-singular-value-decomposition-and-latent-semantic-analysis">2.5.3. Truncated singular value decomposition and latent semantic analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#dictionary-learning">2.5.4. Dictionary Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#factor-analysis">2.5.5. Factor Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#independent-component-analysis-ica">2.5.6. Independent component analysis (ICA)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#non-negative-matrix-factorization-nmf-or-nnmf">2.5.7. Non-negative matrix factorization (NMF or NNMF)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#latent-dirichlet-allocation-lda">2.5.8. Latent Dirichlet Allocation (LDA)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#empirical-covariance">2.6.1. Empirical covariance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#shrunk-covariance">2.6.2. Shrunk Covariance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#sparse-inverse-covariance">2.6.3. Sparse inverse covariance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#robust-covariance-estimation">2.6.4. Robust Covariance Estimation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#overview-of-outlier-detection-methods">2.7.1. Overview of outlier detection methods</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#novelty-detection">2.7.2. Novelty Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#id1">2.7.3. Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#novelty-detection-with-local-outlier-factor">2.7.4. Novelty detection with Local Outlier Factor</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/density.html#density-estimation-histograms">2.8.1. Density Estimation: Histograms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html#kernel-density-estimation">2.8.2. Kernel Density Estimation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html#restricted-boltzmann-machines">2.9.1. Restricted Boltzmann machines</a></li>
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