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dict-17.png
PixelInterleaved width=1015, height=5271, bitDepth=8, colorType=RGBAlpha,...red=8, green=8, blue=8, alpha=8 RGB 8 8 8 8 5271 1 1.0 1 8 8 8...fess.codelibs.org/ja/_images/dict-17.png -
theme.min.css
}[class^=ai-],[class*=" ai-"],[class^=bi-],[class*=" bi-"]{d...t([type=date]):not([type=datetime-local]):not([type=month]):...fess.codelibs.org/_static/assets/css/theme.min.css -
Evaluation of outlier detection estimators — sc...
) s = ( y == 2 ) + ( y == 4 ) X = X . loc [ s ] y = y . loc [...random_state = 42 , return_X_y = True , as_frame = True ) y = ( y !=...scikit-learn.org/stable/auto_examples/miscellaneous/plot_outlier_detection_bench.html -
Illustration of prior and posterior Gaussian pr...
nrows = 2 , sharex = True , sharey = True , figsize = ( 10 ,...nrows = 2 , sharex = True , sharey = True , figsize = ( 10 ,...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html -
GMM covariances — scikit-learn 1.5.1 documentation
bottom = 0.01 , top = 0.95 , hspace = 0.15 , wspace = 0.05 ,...scatterpoints = 1 , loc = "lower right" , prop = dict ( size = 12 ))...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
Classifier comparison — scikit-learn 1.5.1 docu...
C = 0.025 , random_state = 42 ), SVC ( gamma = 2 , C = 1 ,...clf , X , cmap = cm , alpha = 0.8 , ax = ax , eps = 0.5 ) # Plot...scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html -
Probability Calibration for 3-class classificat...
y = make_blobs ( n_samples = 2000 , n_features = 2 , centers...centers = 3 , random_state = 42 , cluster_std = 5.0 ) X_train ,...scikit-learn.org/stable/auto_examples/calibration/plot_calibration_multiclass.html -
BallTree — scikit-learn 1.5.1 documentation
return_distance == False (d,i) if return_distance == True d ndarray...count if count_only == True ind if count_only == False and return_distance...scikit-learn.org/stable/modules/generated/sklearn.neighbors.BallTree.html -
Gradient Boosting Out-of-Bag estimates — scikit...
n_splits = None ): cv = KFold ( n_splits = n_splits ) cv_clf = ensemble...) x1 = random_state . uniform ( size = n_samples ) x2 = random_state...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html -
Understanding the decision tree structure — sci...
iris = load_iris () X = iris . data y = iris . target...y_test = train_test_split ( X , y , random_state = 0 ) clf = DecisionTreeClassifi...scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html
