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  1. check_increasing — scikit-learn 1.6.1 documenta...

    Skip to main content Back to top Ctrl + K GitHub Choose version check_increasing # sklearn.isotonic. check_increasing...
    scikit-learn.org/stable/modules/generated/sklearn.isotonic.check_increasing.html
    Mon Apr 21 17:07:39 UTC 2025
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  2. Model selection with Probabilistic PCA and Fact...

    Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the likelihood of new data can be used for model selection and covariance estimation. Here we compare PCA and...
    scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html
    Sat Apr 19 00:31:21 UTC 2025
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  3. Illustration of Gaussian process classification...

    This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains consi...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html
    Sat Apr 19 00:31:20 UTC 2025
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  4. t-SNE: The effect of various perplexity values ...

    An illustration of t-SNE on the two concentric circles and the S-curve datasets for different perplexity values. We observe a tendency towards clearer shapes as the perplexity value increases. The ...
    scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html
    Sat Apr 19 00:31:21 UTC 2025
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  5. inplace_csr_row_normalize_l1 — scikit-learn 1.6...

    Skip to main content Back to top Ctrl + K GitHub Choose version inplace_csr_row_normalize_l1 # sklearn.utils.sparsefu...
    scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz...
    Sat Apr 19 00:31:22 UTC 2025
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  6. 6.8. Pairwise metrics, Affinities and Kernels —...

    computing the similarity of documents represented as tf-idf vectors....-ouvertes.fr/hal-00171412/document On this page This Page...
    scikit-learn.org/stable/modules/metrics.html
    Sat Apr 19 00:31:21 UTC 2025
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  7. Compare cross decomposition methods — scikit-le...

    Simple usage of various cross decomposition algorithms: PLSCanonical, PLSRegression, with multivariate response, a.k.a. PLS2, PLSRegression, with univariate response, a.k.a. PLS1, CCA. Given 2 mult...
    scikit-learn.org/stable/auto_examples/cross_decomposition/plot_compare_cross_decomposition.html
    Sat Apr 19 00:31:21 UTC 2025
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  8. Decision Tree Regression with AdaBoost — scikit...

    A decision tree is boosted using the AdaBoost.R2 1 algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision trees) is compared with a single decision tre...
    scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html
    Sat Apr 19 00:31:22 UTC 2025
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  9. Gaussian Mixture Model Ellipsoids — scikit-lear...

    Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisation ( GaussianMixture class) and Variational Inference ( BayesianGaussianMixture class models with a ...
    scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html
    Sat Apr 19 00:31:22 UTC 2025
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  10. 6. Dataset transformations — scikit-learn 1.6.1...

    scikit-learn provides a library of transformers, which may clean (see Preprocessing data), reduce (see Unsupervised dimensionality reduction), expand (see Kernel Approximation) or generate (see Fea...
    scikit-learn.org/stable/data_transforms.html
    Sat Apr 19 00:31:22 UTC 2025
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