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  1. load_iris — scikit-learn 1.7.2 documentation

    Gallery examples: Plot classification probability Plot Hierarchical Clustering Dendrogram Concatenating multiple feature extraction methods Incremental PCA Principal Component Analysis (PCA) on Iri...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html
    Sat Oct 11 07:51:27 UTC 2025
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  2. check_memory — scikit-learn 1.7.2 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version check_memory # sklearn.utils.validation. check_memory...
    scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_memory.html
    Sat Oct 11 07:51:26 UTC 2025
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  3. Support Vector Machines — scikit-learn 1.7.2 do...

    Examples concerning the sklearn.svm module. One-class SVM with non-linear kernel (RBF) Plot classification boundaries with different SVM Kernels Plot different SVM classifiers in the iris dataset P...
    scikit-learn.org/stable/auto_examples/svm/index.html
    Sat Oct 11 07:51:26 UTC 2025
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  4. Missing Value Imputation — scikit-learn 1.7.2 d...

    Examples concerning the sklearn.impute module. Imputing missing values before building an estimator Imputing missing values with variants of IterativeImputer
    scikit-learn.org/stable/auto_examples/impute/index.html
    Sat Oct 11 07:51:25 UTC 2025
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  5. k_means — scikit-learn 1.7.2 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version k_means # sklearn.cluster. k_means ( X , n_clusters ,...
    scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html
    Sat Oct 11 07:51:26 UTC 2025
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  6. Species distribution modeling — scikit-learn 1....

    Modeling species’ geographic distributions is an important problem in conservation biology. In this example, we model the geographic distribution of two South American mammals given past observatio...
    scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html
    Sat Oct 11 07:51:25 UTC 2025
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  7. Probability calibration of classifiers — scikit...

    When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However...
    scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html
    Sat Oct 11 07:51:27 UTC 2025
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  8. load_digits — scikit-learn 1.7.2 documentation

    Gallery examples: Recognizing hand-written digits Feature agglomeration Various Agglomerative Clustering on a 2D embedding of digits A demo of K-Means clustering on the handwritten digits data Sele...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html
    Sat Oct 11 07:51:25 UTC 2025
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  9. GMM Initialization Methods — scikit-learn 1.7.2...

    Examples of the different methods of initialization in Gaussian Mixture Models See Gaussian mixture models for more information on the estimator. Here we generate some sample data with four easy to...
    scikit-learn.org/stable/auto_examples/mixture/plot_gmm_init.html
    Sat Oct 11 07:51:27 UTC 2025
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  10. Univariate Feature Selection — scikit-learn 1.7...

    This notebook is an example of using univariate feature selection to improve classification accuracy on a noisy dataset. In this example, some noisy (non informative) features are added to the iris...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html
    Sat Oct 11 07:51:25 UTC 2025
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