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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.cross_decomposition — scikit-learn 1.8....
Algorithms for cross decomposition. User guide. See the Cross decomposition section for further details.scikit-learn.org/stable/api/sklearn.cross_decomposition.html -
sklearn.base — scikit-learn 1.8.0 documentation
scikit-learn.org/stable/api/sklearn.base.html -
sklearn.compose — scikit-learn 1.8.0 documentation
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 -
sklearn.discriminant_analysis — scikit-learn 1....
Linear and quadratic discriminant analysis. User guide. See the Linear and Quadratic Discriminant Analysis section for further details.scikit-learn.org/stable/api/sklearn.discriminant_analysis.html -
sklearn.isotonic — scikit-learn 1.8.0 documenta...
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 -
Examples based on real world datasets — scikit-...
scikit-learn.org/stable/auto_examples/applications/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.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 -
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