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  1. Visualizing cross-validation behavior in scikit...

    scikit-learn 1.4 Release Highlights for scikit-learn 1.4 Gallery...10 ) percentiles_classes = [ 0.1 , 0.3 , 0.6 ] y = np . hstack...
    scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html
    Mon Mar 23 20:39:22 UTC 2026
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  2. Cross decomposition — scikit-learn 1.8.0 docume...

    Examples concerning the sklearn.cross_decomposition module. Compare cross decomposition methods Principal Component Regression vs Partial Least Squares Regression
    scikit-learn.org/stable/auto_examples/cross_decomposition/index.html
    Mon Mar 23 20:39:22 UTC 2026
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  3. Frozen Estimators — scikit-learn 1.8.0 document...

    Examples concerning the sklearn.frozen module. Examples of Using FrozenEstimator
    scikit-learn.org/stable/auto_examples/frozen/index.html
    Mon Mar 23 20:39:22 UTC 2026
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  4. Nearest Neighbors — scikit-learn 1.8.0 document...

    Examples concerning the sklearn.neighbors module. Approximate nearest neighbors in TSNE Caching nearest neighbors Comparing Nearest Neighbors with and without Neighborhood Components Analysis Dimen...
    scikit-learn.org/stable/auto_examples/neighbors/index.html
    Mon Mar 23 20:39:21 UTC 2026
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  5. sklearn.model_selection — scikit-learn 1.8.0 do...

    Tools for model selection, such as cross validation and hyper-parameter tuning. User guide. See the Cross-validation: evaluating estimator performance, Tuning the hyper-parameters of an estimator, ...
    scikit-learn.org/stable/api/sklearn.model_selection.html
    Mon Mar 23 20:39:20 UTC 2026
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  6. sklearn.feature_selection — scikit-learn 1.8.0 ...

    Feature selection algorithms. These include univariate filter selection methods and the recursive feature elimination algorithm. User guide. See the Feature selection section for further details.
    scikit-learn.org/stable/api/sklearn.feature_selection.html
    Mon Mar 23 20:39:20 UTC 2026
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  7. sklearn.neural_network — scikit-learn 1.8.0 doc...

    Models based on neural networks. User guide. See the Neural network models (supervised) and Neural network models (unsupervised) sections for further details.
    scikit-learn.org/stable/api/sklearn.neural_network.html
    Mon Mar 23 20:39:20 UTC 2026
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  8. sklearn.exceptions — scikit-learn 1.8.0 documen...

    Custom warnings and errors used across scikit-learn.
    scikit-learn.org/stable/api/sklearn.exceptions.html
    Mon Mar 23 20:39:21 UTC 2026
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  9. sklearn.experimental — scikit-learn 1.8.0 docum...

    Importable modules that enable the use of experimental features or estimators.
    scikit-learn.org/stable/api/sklearn.experimental.html
    Mon Mar 23 20:39:20 UTC 2026
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  10. sklearn.metrics — scikit-learn 1.8.0 documentation

    Score functions, performance metrics, pairwise metrics and distance computations. User guide. See the Metrics and scoring: quantifying the quality of predictions and Pairwise metrics, Affinities an...
    scikit-learn.org/stable/api/sklearn.metrics.html
    Mon Mar 23 20:39:23 UTC 2026
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