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sklearn.impute — scikit-learn 1.8.0 docum...
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.gaussian_process — scikit-learn 1...
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.isotonic — scikit-learn 1.8.0 doc...
Isotonic regression for obtaining monotonic fit to data. User guide. See the Isotonic regression section for further details.scikit-learn.org/stable/api/sklearn.isotonic.html -
sklearn.compose — scikit-learn 1.8.0 docu...
Meta-estimators for building composite models with transformers. In addition to its current contents, this module will eventually be home to refurbished versions of Pipeline and FeatureUnion. User ...scikit-learn.org/stable/api/sklearn.compose.html -
GMM covariances — scikit-learn 1.8.0 docu...
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 -
Developing Estimators — scikit-learn 1.8....
Examples concerning the development of Custom Estimator.__sklearn_is_fitted__ as Developer APIscikit-learn.org/stable/auto_examples/developing_estimators/index.html -
Incremental PCA — scikit-learn 1.8.0 docu...
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 -
Feature discretization — scikit-learn 1.8...
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 -
Kernel Approximation — scikit-learn 1.8.0...
Examples concerning the sklearn.kernel_approximation module. Scalable learning with polynomial kernel approximationscikit-learn.org/stable/auto_examples/kernel_approximation/index.html -
1.17. Neural network models (supervised) —...
details can be found in the documentation of SGD Adam is similar to...scikit-learn.org/stable/modules/neural_networks_supervised.html