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Incremental PCA — scikit-learn 1.8.0 documentation
Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit in memory. IPCA build...scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html -
Covariance estimation — scikit-learn 1.8.0 docu...
Examples concerning the sklearn.covariance module. Ledoit-Wolf vs OAS estimation Robust covariance estimation and Mahalanobis distances relevance Robust vs Empirical covariance estimate Shrinkage c...scikit-learn.org/stable/auto_examples/covariance/index.html -
GMM covariances — scikit-learn 1.8.0 documentation
Demonstration of several covariances types for Gaussian mixture models. See Gaussian mixture models for more information on the estimator. Although GMM are often used for clustering, we can compare...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
Kernel Approximation — scikit-learn 1.8.0 docum...
Examples concerning the sklearn.kernel_approximation module. Scalable learning with polynomial kernel approximationscikit-learn.org/stable/auto_examples/kernel_approximation/index.html -
Feature discretization — scikit-learn 1.8.0 doc...
A demonstration of feature discretization on synthetic classification datasets. Feature discretization decomposes each feature into a set of bins, here equally distributed in width. The discrete va...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html -
sklearn.datasets — scikit-learn 1.8.0 documenta...
Utilities to load popular datasets and artificial data generators. User guide. See the Dataset loading utilities section for further details. Loaders: Sample generators:scikit-learn.org/stable/api/sklearn.datasets.html -
sklearn.gaussian_process — scikit-learn 1.8.0 d...
Gaussian process based regression and classification. User guide. See the Gaussian Processes section for further details. Kernels: A set of kernels that can be combined by operators and used in Gau...scikit-learn.org/stable/api/sklearn.gaussian_process.html -
sklearn.impute — scikit-learn 1.8.0 documentation
Transformers for missing value imputation. User guide. See the Imputation of missing values section for further details.scikit-learn.org/stable/api/sklearn.impute.html -
sklearn.base — scikit-learn 1.8.0 documentation
Base classes for all estimators and various utility functions.scikit-learn.org/stable/api/sklearn.base.html -
sklearn.linear_model — scikit-learn 1.8.0 docum...
A variety of linear models. User guide. See the Linear Models section for further details. The following subsections are only rough guidelines: the same estimator can fall into multiple categories,...scikit-learn.org/stable/api/sklearn.linear_model.html