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Effect of transforming the targets in regressio...
y = make_regression ( n_samples = 10_000 , noise = 100 ,...ax1 ) = plt . subplots ( 1 , 2 , sharey = True ) ridge_cv = RidgeCV...scikit-learn.org/stable/auto_examples/compose/plot_transformed_target.html -
RocCurveDisplay — scikit-learn 1.7.0 documentation
( fpr = fpr , tpr = tpr , roc_auc = roc_auc , ... name = 'example...pos_label = None , ax = None , name = None , curve_kwargs = None ,...scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html -
Novelty detection with Local Outlier Factor (LO...
X_outliers = np . random . uniform ( low =- 4 , high = 4 , size = ( 20...1 ], c = "blueviolet" , s = s , edgecolors = "k" ) c = plt . scatter...scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html -
Comparing random forests and the multi-output m...
edgecolor = "k" , c = "navy" , s = s , marker = "s" , alpha = a , label...edgecolor = "k" , c = "c" , s = s , marker = "^" , alpha = a , label...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html -
DetCurveDisplay — scikit-learn 1.7.0 documentation
test_size = 0.4 , random_state = 0 ) >>> clf = SVC ( random_state...DetCurveDisplay ( ... fpr = fpr , fnr = fnr , estimator_name = "SVC" ... )...scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html -
Probability calibration of classifiers — scikit...
): this_X = X_train [ y_train == this_y ] this_sw = sw_train [...n_samples = n_samples , centers = centers , shuffle = False , random_state...scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html -
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Demonstration of k-means assumptions — scikit-l...
( X [ y == 0 ][: 500 ], X [ y == 1 ][: 100 ], X [ y == 2 ][: 10...axs = plt . subplots ( nrows = 2 , ncols = 2 , figsize = ( 12...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html