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  1. make_classification — scikit-learn 1.5.2 docume...

    class_sep = 1.0 , hypercube = True , shift = 0.0 , scale = 1.0 , shuffle...[np.int64(0), np.int64(0), np.int64(1), np.int64(1), np.int64(0)] Gallery...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html
    Wed Oct 30 20:01:23 UTC 2024
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  2. spectral_clustering — scikit-learn 1.5.2 docume...

    array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7...random_state = 0 ... ) array([1, 1, 1, 0, 0, 0]) Gallery examples...
    scikit-learn.org/stable/modules/generated/sklearn.cluster.spectral_clustering.html
    Wed Oct 30 20:01:23 UTC 2024
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  3. PolynomialFeatures — scikit-learn 1.5.2 documen...

    fit_transform ( X ) array([[ 1., 0., 1., 0., 0., 1.], [ 1., 2., 3., 4., 6.,...) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5., 20.]])...
    scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html
    Wed Oct 30 20:01:23 UTC 2024
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  4. enet_path — scikit-learn 1.5.2 documentation

    l1_ratio * || w || _1 + 0.5 * alpha * ( 1 - l1_ratio ) * || w...0.56...], [ 0. , 1.12..., 0.61...], [-0. , -2.12..., -1.12...], [ 0....
    scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html
    Wed Oct 30 20:01:21 UTC 2024
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  5. RepeatedKFold — scikit-learn 1.5.2 documentation

    array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> rkf = RepeatedKFold (...
    scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html
    Wed Oct 30 20:01:21 UTC 2024
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  6. chi2 — scikit-learn 1.5.2 documentation

    array ([[ 1 , 1 , 3 ], ... [ 0 , 1 , 5 ], ... [ 5 , 4 , 1 ], ......y = np . array ([ 1 , 1 , 0 , 0 , 2 , 2 ]) >>> chi2_stats , p_values...
    scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html
    Wed Oct 30 20:01:21 UTC 2024
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  7. RBFSampler — scikit-learn 1.5.2 documentation

    [ 1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...in version 1.2: The option "scale" was added in 1.2. n_components...
    scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html
    Wed Oct 30 20:01:23 UTC 2024
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  8. f_regression — scikit-learn 1.5.2 documentation

    r_regression values lie in [-1, 1] and can thus be negative. f_regression...set to 0.0 . Added in version 1.1. Returns : f_statistic ndarray...
    scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html
    Wed Oct 30 20:01:21 UTC 2024
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  9. 1.11. Ensembles: Gradient boosting, random fore...

    Gradient Boosting models 1.11.1.1.1. Usage # Most of the parameters...= [[ 1 , 0 ], ... [ 1 , 0 ], ... [ 1 , 0 ], ... [ 0 , 1 ]] >>>...
    scikit-learn.org/stable/modules/ensemble.html
    Wed Oct 30 20:01:21 UTC 2024
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  10. LatentDirichletAllocation — scikit-learn 1.5.2 ...

    evaluate_every = -1 , total_samples = 1000000.0 , perp_tol = 0.1 , mean_change_tol...None, defaults to 1 / n_components . In [1] , this is called...
    scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
    Wed Oct 30 20:01:21 UTC 2024
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