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  1. 5. Inspection — scikit-learn 1.7.1 documentation

    Predictive performance is often the main goal of developing machine learning models. Yet summarizing performance with an evaluation metric is often insufficient: it assumes that the evaluation metr...
    scikit-learn.org/stable/inspection.html
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  2. 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
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  3. 12. Dispatching — scikit-learn 1.7.1 documentation

    Array API support (experimental)- Example usage, Support for Array API-compatible inputs, Input and output array type handling, Common estimator checks..
    scikit-learn.org/stable/dispatching.html
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  4. Missing Value Imputation — scikit-learn 1.7.1 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
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  5. 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
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  6. fbeta_score — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version fbeta_score # sklearn.metrics. fbeta_score ( y_true ,...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html
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  7. partial_dependence — scikit-learn 1.7.1 documen...

    Gallery examples: Partial Dependence and Individual Conditional Expectation Plots
    scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html
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  8. sklearn.preprocessing — scikit-learn 1.7.1 docu...

    Methods for scaling, centering, normalization, binarization, and more. User guide. See the Preprocessing data section for further details.
    scikit-learn.org/stable/api/sklearn.preprocessing.html
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  9. config_context — scikit-learn 1.7.1 documentation

    Gallery examples: Introducing the set_output API
    scikit-learn.org/stable/modules/generated/sklearn.config_context.html
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  10. k_means — scikit-learn 1.7.1 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
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