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Results 681 - 690 of 2,956 for 1 (1.79 sec)

  1. Comparing different clustering algorithms on to...

    "connectivity matrix is [0-9]{1,2}" + " > 1. Completing it to avoid...= n_samples , cluster_std = [ 1.0 , 2.5 , 0.5 ], random_state...
    scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
    Mon Apr 21 17:07:38 UTC 2025
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  2. calinski_harabasz_score — scikit-learn 1.6.1 do...

    References [ 1 ] T. Calinski and J. Harabasz,...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.calinski_harabasz_score.html
    Mon Apr 21 17:07:40 UTC 2025
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  3. Probability Calibration for 3-class classificat...

    "($\frac {1}{3} $, $\frac {1}{3} $, $\frac {1}{3} $)" , xy = ( 1.0 /...r "($1$, $0$, $0$)" , xy = ( 1 , 0 ), xytext = ( 1 , 0.1 ), xycoords...
    scikit-learn.org/stable/auto_examples/calibration/plot_calibration_multiclass.html
    Mon Apr 21 17:07:38 UTC 2025
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  4. Face completion with a multi-output estimators ...

    shape [ 1 ] # Upper half of the faces X_train...X_train = train [:, : ( n_pixels + 1 ) // 2 ] # Lower half of the faces...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html
    Mon Apr 21 17:07:39 UTC 2025
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  5. ledoit_wolf_shrinkage — scikit-learn 1.6.1 docu...

    regularized (shrunk) covariance is: (1 - shrinkage) * cov + shrinkage...
    scikit-learn.org/stable/modules/generated/sklearn.covariance.ledoit_wolf_shrinkage.html
    Mon Apr 21 17:07:39 UTC 2025
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  6. Faces recognition example using eigenfaces and ...

    1 )[ - 1 ] true_name = target_names...y_test [ i ]] . rsplit ( " " , 1 )[ - 1 ] return "predicted: %s \n...
    scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html
    Mon Apr 21 17:07:39 UTC 2025
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  7. SVM: Separating hyperplane for unbalanced class...

    clusters of random points n_samples_1 = 1000 n_samples_2 = 100 centers...2.0 , 2.0 ]] clusters_std = [ 1.5 , 0.5 ] X , y = make_blobs (...
    scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html
    Mon Apr 21 17:07:39 UTC 2025
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  8. SGD: Maximum margin separating hyperplane — sci...

    linspace ( - 1 , 5 , 10 ) yy = np . linspace ( - 1 , 5 , 10 ) X1...= p [ 0 ] levels = [ - 1.0 , 0.0 , 1.0 ] linestyles = [ "dashed"...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html
    Mon Apr 21 17:07:38 UTC 2025
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  9. Feature agglomeration vs. univariate selection ...

    0 ][ 1 ] . inverse_transform ( coef_ . reshape ( 1 , - 1 )) coef_selection_...roi_size ] = - 1.0 coef [ - roi_size :, - roi_size :] = 1.0 X = np...
    scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection....
    Mon Apr 21 17:07:38 UTC 2025
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  10. randomized_range_finder — scikit-learn 1.6.1 do...

    array ([[ 1 , 2 , 3 ], [ 4 , 5 , 6 ], [ 7...
    scikit-learn.org/stable/modules/generated/sklearn.utils.extmath.randomized_range_finder.html
    Mon Apr 21 17:07:40 UTC 2025
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