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  1. 1.13. Feature selection — scikit-learn 1.6.1 do...

    >>> lsvc = LinearSVC ( C = 0.01 , penalty = "l1" , dual = False...model = SelectFromModel ( lsvc , prefit = True ) >>> X_new = model...
    scikit-learn.org/stable/modules/feature_selection.html
    Sat Apr 19 00:31:22 UTC 2025
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  2. silhouette_score — scikit-learn 1.6.1 documenta...

    metric = 'euclidean' , sample_size = None , random_state = None...X , y = make_blobs ( random_state = 42 ) >>> kmeans = KMeans...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html
    Sat Apr 19 00:31:21 UTC 2025
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  3. make_biclusters — scikit-learn 1.6.1 documentation

    noise = 0.0 , minval = 10 , maxval = 100 , shuffle = True ,..., rows , cols = make_biclusters ( ... shape = ( 10 , 20 ), n_clusters...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_biclusters.html
    Sat Apr 19 00:31:22 UTC 2025
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  4. Robust linear model estimation using RANSAC — s...

    linear_model n_samples = 1000 n_outliers = 50 X , y , coef = datasets . make_regression...n_samples = n_samples , n_features = 1 , n_informative = 1 , noise...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html
    Sat Apr 19 00:31:22 UTC 2025
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  5. 1.6. Nearest Neighbors — scikit-learn 1.6.1 doc...

    nbrs = NearestNeighbors ( n_neighbors = 2 , algorithm = 'ball_tree'...]]) >>> kdt = KDTree ( X , leaf_size = 30 , metric = 'euclidean'...
    scikit-learn.org/stable/modules/neighbors.html
    Sat Apr 19 00:31:22 UTC 2025
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  6. RobustScaler — scikit-learn 1.6.1 documentation

    with_centering = True , with_scaling = True , quantile_range = (25.0,...(25.0, 75.0) , copy = True , unit_variance = False ) [source] # Scale...
    scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html
    Sat Apr 19 00:31:21 UTC 2025
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  7. Robust vs Empirical covariance estimate — sciki...

    n_samples = 80 n_features = 5 repeat = 10 range_n_outliers = np ....repeat ), label = "Robust location" , lw = lw , color = "m" , ) plt...
    scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.html
    Sat Apr 19 00:31:22 UTC 2025
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  8. LabelBinarizer — scikit-learn 1.6.1 documentation

    neg_label = 0 , pos_label = 1 , sparse_output = False ) [source]...Parameters : neg_label int, default=0 Value with which negative labels...
    scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html
    Sat Apr 19 00:31:21 UTC 2025
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  9. QuadraticDiscriminantAnalysis — scikit-learn 1....

    priors = None , reg_param = 0.0 , store_covariance = False ,...>>> y = np . array ([ 1 , 1 , 1 , 2 , 2 , 2 ]) >>> clf = QuadraticDiscriminan...
    scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnal...
    Sat Apr 19 00:31:21 UTC 2025
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  10. sample_without_replacement — scikit-learn 1.6.1...

    default=’auto’ If method == “auto”, the ratio of...n_samples , method = 'auto' , random_state = None ) # Sample integers...
    scikit-learn.org/stable/modules/generated/sklearn.utils.random.sample_without_replacement.html
    Sat Apr 19 00:31:20 UTC 2025
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