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  1. TfidfTransformer — scikit-learn 1.8.0 documenta...

    array([[1, 1, 1, 1, 0, 1, 0, 0], [1, 2, 0, 1, 1, 1, 0, 0], [1, 0,...0, 0, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0]]) >>> pipe [ 'tfid'...
    scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html
    Mon Mar 23 20:39:23 UTC 2026
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  2. LocallyLinearEmbedding — scikit-learn 1.8.0 doc...

    reg = 0.001 , eigen_solver = 'auto' , tol = 1e-06 , max_iter...max_iter = 100 , method = 'standard' , hessian_tol = 0.0001 , modified_tol...
    scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html
    Mon Mar 23 20:39:21 UTC 2026
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  3. SelectPercentile — scikit-learn 1.8.0 documenta...

    Added in version 0.18. percentile int, default=10 Percent of features...all strings. Added in version 1.0. See also f_classif ANOVA F-value...
    scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectPercentile.html
    Mon Mar 23 20:39:23 UTC 2026
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  4. VarianceThreshold — scikit-learn 1.8.0 document...

    X = [[ 0 , 2 , 0 , 3 ], [ 0 , 1 , 4 , 3 ], [ 0 , 1 , 1 , 3 ]]...fit_transform ( X ) array([[2, 0], [1, 4], [1, 1]]) fit ( X , y = None...
    scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
    Mon Mar 23 20:39:23 UTC 2026
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  5. SkewedChi2Sampler — scikit-learn 1.8.0 document...

    [[ 0 , 0 ], [ 1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y = [ 0 , 0...( * , skewedness = 1.0 , n_components = 100 , random_state = None...
    scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.SkewedChi2Sampler.html
    Mon Mar 23 20:39:20 UTC 2026
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  6. trustworthiness — scikit-learn 1.8.0 documentation

    trustworthiness is within [0, 1]. It is defined as \[T(k) = 1 - \frac{2}{nk...trustworthiness to lies within [0, 1], as mentioned in [1] . An error will be...
    scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html
    Mon Mar 23 20:39:21 UTC 2026
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  7. img_to_graph — scikit-learn 1.8.0 documentation

    array([[0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 1, 1, 1]]) On...>>> img = np . array ([[ 0 , 0 ], [ 0 , 1 ]]) >>> img_to_graph (...
    scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.img_to_graph.html
    Mon Mar 23 20:39:21 UTC 2026
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  8. TSNE — scikit-learn 1.8.0 documentation

    ([[ 0 , 0 , 0 ], [ 0 , 1 , 1 ], [ 1 , 0 , 1 ], [ 1 , 1 , 1 ]])...is usually in the range [10.0, 1000.0]. If the learning rate is...
    scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
    Mon Mar 23 20:39:20 UTC 2026
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  9. mean_gamma_deviance — scikit-learn 1.8.0 docume...

    0.5 , 1 , 4 ] >>> y_pred = [ 0.5 , 0.5 , 2. , 2....point value (the best value is 0.0). Examples >>> from sklearn.metrics...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_gamma_deviance.html
    Mon Mar 23 20:39:21 UTC 2026
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  10. hinge_loss — scikit-learn 1.8.0 documentation

    hinge_loss >>> X = [[ 0 ], [ 1 ]] >>> y = [ - 1 , 1 ] >>> est = svm...hinge_loss ([ - 1 , 1 , 1 ], pred_decision ) 0.30 In the multiclass...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html
    Mon Mar 23 20:39:23 UTC 2026
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