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  1. sklearn.covariance — scikit-learn 1.6.1 documen...

    Methods and algorithms to robustly estimate covariance. They estimate the covariance of features at given sets of points, as well as the precision matrix defined as the inverse of the covariance. C...
    scikit-learn.org/stable/api/sklearn.covariance.html
    Sat Apr 19 00:31:21 UTC 2025
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  2. 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
    Sat Apr 19 00:31:21 UTC 2025
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  3. Recursive feature elimination — scikit-learn 1....

    This example demonstrates how Recursive Feature Elimination ( RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html
    Sat Apr 19 00:31:22 UTC 2025
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  4. Gradient Boosting regression — scikit-learn 1.6...

    This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems. Here,...
    scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html
    Sat Apr 19 00:31:22 UTC 2025
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  5. Polynomial and Spline interpolation — scikit-le...

    This example demonstrates how to approximate a function with polynomials up to degree degree by using ridge regression. We show two different ways given n_samples of 1d points x_i: PolynomialFeatur...
    scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html
    Sat Apr 19 00:31:22 UTC 2025
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  6. Generalized Linear Models — scikit-learn 1.6.1 ...

    Examples concerning the sklearn.linear_model module. Comparing Linear Bayesian Regressors Comparing various online solvers Curve Fitting with Bayesian Ridge Regression Decision Boundaries of Multin...
    scikit-learn.org/stable/auto_examples/linear_model/index.html
    Sat Apr 19 00:31:22 UTC 2025
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  7. make_circles — scikit-learn 1.6.1 documentation

    Gallery examples: Classifier comparison Comparing different clustering algorithms on toy datasets Comparing different hierarchical linkage methods on toy datasets Kernel PCA Hashing feature transfo...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html
    Sat Apr 19 00:31:22 UTC 2025
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  8. make_moons — scikit-learn 1.6.1 documentation

    Gallery examples: Classifier comparison Comparing different clustering algorithms on toy datasets Comparing different hierarchical linkage methods on toy datasets Comparing anomaly detection algori...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
    Sat Apr 19 00:31:22 UTC 2025
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  9. 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
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  10. roc_curve — scikit-learn 1.6.1 documentation

    Gallery examples: Species distribution modeling Visualizations with Display Objects Detection error tradeoff (DET) curve Multiclass Receiver Operating Characteristic (ROC)
    scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
    Sat Apr 19 00:31:22 UTC 2025
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