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  1. randomized_svd — scikit-learn 1.6.0 documentation

    approximation problem described in [1] (problem (1.5), p5). Refer to Wikipedia...n_iter=0 or 1 should even work fine in theory (see [1] page 9)....
    scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_svd.html
    Fri Dec 20 11:14:14 UTC 2024
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  2. det_curve — scikit-learn 1.6.0 documentation

    y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise...labels are not either {-1, 1} or {0, 1}, then pos_label should...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.det_curve.html
    Fri Dec 20 11:14:14 UTC 2024
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  3. coverage_error — scikit-learn 1.6.0 documentation

    y_true = [[ 1 , 0 , 0 ], [ 0 , 1 , 1 ]] >>> y_score = [[ 1 , 0 , 0...0 ], [ 0 , 1 , 1 ]] >>> coverage_error ( y_true , y_score ) np.float64(1.5)...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.coverage_error.html
    Fri Dec 20 11:14:14 UTC 2024
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  4. make_friedman1 — scikit-learn 1.6.0 documentation

    Annals of Statistics 19 (1), pages 1-67, 1991. [ 2 ] L. Breiman,...[source] # Generate the “Friedman #1” regression problem. This dataset...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html
    Fri Dec 20 11:14:14 UTC 2024
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  5. Gradient Boosting regression — scikit-learn 1.6...

    subplot ( 1 , 1 , 1 ) plt . title ( "Deviance"...12 , 6 )) plt . subplot ( 1 , 2 , 1 ) plt . barh ( pos , feature_importance...
    scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html
    Fri Dec 20 11:14:14 UTC 2024
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  6. log_loss — scikit-learn 1.6.0 documentation

    p) = -(y \log (p) + (1 - y) \log (1 - p))\] Read more in the..., "spam" ], ... [[ .1 , .9 ], [ .9 , .1 ], [ .8 , .2 ], [ .35...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html
    Fri Dec 20 11:14:16 UTC 2024
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  7. dcg_score — scikit-learn 1.6.0 documentation

    asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...to have a score between 0 and 1. References Wikipedia entry for...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.dcg_score.html
    Fri Dec 20 11:14:14 UTC 2024
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  8. power_transform — scikit-learn 1.6.0 documentation

    [[-1.332... -0.707...] [ 0.256... -0.707...] [ 1.076... 1.414...]]...Available methods are: ‘yeo-johnson’ [1] , works with positive and negative...
    scikit-learn.org/stable/modules/generated/sklearn.preprocessing.power_transform.html
    Thu Dec 12 16:37:36 UTC 2024
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  9. make_circles — scikit-learn 1.6.0 documentation

    int64(1), np.int64(1), np.int64(1), np.int64(0), np.int64(0)]...outer circle in the range [0, 1) . Returns : X ndarray of shape...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html
    Fri Dec 20 11:14:16 UTC 2024
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  10. Importance of Feature Scaling — scikit-learn 1....

    0 and 1,000; whereas the variable “hue” varies between 1 and 10....it has a standard deviation of 1 and a mean of 0. Even if tree...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html
    Fri Dec 20 11:14:18 UTC 2024
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