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minmax_scale — scikit-learn 1.8.0 documen...
Gallery examples: Restricted Boltzmann Machine features for digit classification Compare the effect of different scalers on data with outliersscikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html -
LabelPropagation — scikit-learn 1.8.0 doc...
Skip to main content Back to top Ctrl + K GitHub Choose version LabelPropagation # class sklearn.semi_supervised. Lab...scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html -
GaussianRandomProjection — scikit-learn 1...
Skip to main content Back to top Ctrl + K GitHub Choose version GaussianRandomProjection # class sklearn.random_proje...scikit-learn.org/stable/modules/generated/sklearn.random_projection.GaussianRandomProjection.html -
resample — scikit-learn 1.8.0 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version resample # sklearn.utils. resample ( * arrays , repla...scikit-learn.org/stable/modules/generated/sklearn.utils.resample.html -
Model Selection — scikit-learn 1.8.0 documentation
Examples related to the sklearn.model_selection module. Balance model complexity and cross-validated score Class Likelihood Ratios to measure classification performance Comparing randomized search ...scikit-learn.org/stable/auto_examples/model_selection/index.html -
Kernel PCA — scikit-learn 1.8.0 documentation
This example shows the difference between the Principal Components Analysis ( PCA) and its kernelized version ( KernelPCA). On the one hand, we show that KernelPCA is able to find a projection of t...scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html -
sklearn.decomposition — scikit-learn 1.8.0 docu...
Matrix decomposition algorithms. These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be regarded as dimensionality reduction techniques. User guide. See the Decomposing...scikit-learn.org/stable/api/sklearn.decomposition.html -
sklearn.calibration — scikit-learn 1.8.0 docume...
Methods for calibrating predicted probabilities. User guide. See the Probability calibration section for further details. Visualization:scikit-learn.org/stable/api/sklearn.calibration.html -
sklearn.cluster — scikit-learn 1.8.0 documentation
Popular unsupervised clustering algorithms. User guide. See the Clustering and Biclustering sections for further details.scikit-learn.org/stable/api/sklearn.cluster.html -
Version 1.0 — scikit-learn 1.8.0 documentation
previously didn’t work as documented – or according to reasonable...these functions were not documented and part from the public...scikit-learn.org/stable/whats_new/v1.0.html