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  1. enable_iterative_imputer — scikit-learn 1.7.2 d...

    Enables IterativeImputer The API and results of this estimator might change without any deprecation cycle. Importing this file dynamically sets IterativeImputer as an attribute of the impute module:
    scikit-learn.org/stable/modules/generated/sklearn.experimental.enable_iterative_imputer.html
    Sat Nov 01 09:15:33 UTC 2025
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  2. precision_recall_curve — scikit-learn 1.7.2 doc...

    Gallery examples: Visualizations with Display Objects Precision-Recall
    scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
    Sat Nov 01 09:15:33 UTC 2025
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  3. fetch_lfw_people — scikit-learn 1.7.2 documenta...

    Gallery examples: Faces recognition example using eigenfaces and SVMs
    scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html
    Sat Nov 01 09:15:34 UTC 2025
      114.8K bytes
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  4. orthogonal_mp_gram — scikit-learn 1.7.2 documen...

    Skip to main content Back to top Ctrl + K GitHub Choose version orthogonal_mp_gram # sklearn.linear_model. orthogonal...
    scikit-learn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html
    Sat Nov 01 09:15:33 UTC 2025
      113.8K bytes
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  5. sklearn.feature_selection — scikit-learn 1.7.2 ...

    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
    Sat Nov 01 09:15:33 UTC 2025
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  6. check_random_state — scikit-learn 1.7.2 documen...

    Gallery examples: Empirical evaluation of the impact of k-means initialization MNIST classification using multinomial logistic + L1 Manifold Learning methods on a severed sphere Isotonic Regression...
    scikit-learn.org/stable/modules/generated/sklearn.utils.check_random_state.html
    Sat Nov 01 09:15:33 UTC 2025
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  7. sklearn.model_selection — scikit-learn 1.7.2 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
    Sat Nov 01 09:15:32 UTC 2025
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  8. mutual_info_score — scikit-learn 1.7.2 document...

    Gallery examples: Adjustment for chance in clustering performance evaluation
    scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html
    Sat Nov 01 09:15:33 UTC 2025
      111.7K bytes
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  9. l1_min_c — scikit-learn 1.7.2 documentation

    Gallery examples: Regularization path of L1- Logistic Regression
    scikit-learn.org/stable/modules/generated/sklearn.svm.l1_min_c.html
    Sat Nov 01 09:15:34 UTC 2025
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  10. get_data_home — scikit-learn 1.7.2 documentation

    Gallery examples: Out-of-core classification of text documents
    scikit-learn.org/stable/modules/generated/sklearn.datasets.get_data_home.html
    Sat Nov 01 09:15:34 UTC 2025
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