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  1. log_loss — scikit-learn 1.6.1 documentation

    Gallery examples: Probability Calibration curves Probability Calibration for 3-class classification Gradient Boosting Out-of-Bag estimates Gradient Boosting regularization Probabilistic predictions...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html
    Mon Apr 21 17:07:39 UTC 2025
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  2. dcg_score — scikit-learn 1.6.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version dcg_score # sklearn.metrics. dcg_score ( y_true , y_s...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.dcg_score.html
    Mon Apr 21 17:07:39 UTC 2025
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  3. sklearn.inspection — scikit-learn 1.6.1 documen...

    Tools for model inspection. User guide. See the Inspection section for further details. Plotting:
    scikit-learn.org/stable/api/sklearn.inspection.html
    Mon Apr 21 17:07:38 UTC 2025
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  4. coverage_error — scikit-learn 1.6.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version coverage_error # sklearn.metrics. coverage_error ( y_...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.coverage_error.html
    Mon Apr 21 17:07:39 UTC 2025
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  5. check_cv — scikit-learn 1.6.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version check_cv # sklearn.model_selection. check_cv ( cv = 5...
    scikit-learn.org/stable/modules/generated/sklearn.model_selection.check_cv.html
    Mon Apr 21 17:07:39 UTC 2025
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  6. plot_tree — scikit-learn 1.6.1 documentation

    Gallery examples: Plot the decision surface of decision trees trained on the iris dataset Understanding the decision tree structure
    scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html
    Mon Apr 21 17:07:39 UTC 2025
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  7. sklearn.ensemble — scikit-learn 1.6.1 documenta...

    Ensemble-based methods for classification, regression and anomaly detection. User guide. See the Ensembles: Gradient boosting, random forests, bagging, voting, stacking section for further details.
    scikit-learn.org/stable/api/sklearn.ensemble.html
    Mon Apr 21 17:07:38 UTC 2025
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  8. rand_score — scikit-learn 1.6.1 documentation

    Gallery examples: Adjustment for chance in clustering performance evaluation
    scikit-learn.org/stable/modules/generated/sklearn.metrics.rand_score.html
    Mon Apr 21 17:07:39 UTC 2025
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  9. r2_score — scikit-learn 1.6.1 documentation

    Gallery examples: L1-based models for Sparse Signals Non-negative least squares Ordinary Least Squares Example Failure of Machine Learning to infer causal effects Effect of transforming the targets...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html
    Mon Apr 21 17:07:40 UTC 2025
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  10. sklearn.manifold — scikit-learn 1.6.1 documenta...

    Data embedding techniques. User guide. See the Manifold learning section for further details.
    scikit-learn.org/stable/api/sklearn.manifold.html
    Mon Apr 21 17:07:38 UTC 2025
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