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MLPClassifier — scikit-learn 1.8.0 documentation
array([1, 0, 1, 0, 1]) >>> clf . score ( X_test , y_test ) 0.8......validation_fraction = 0.1 , beta_1 = 0.9 , beta_2 = 0.999 , epsilon = 1e-08 , n_iter_no_change...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html -
GaussianNB — scikit-learn 1.8.0 documentation
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...2 , 1 ], [ 3 , 2 ]]) >>> Y = np . array ([ 1 , 1 , 1 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html -
RANSACRegressor — scikit-learn 1.8.0 documentation
min_samples int (>= 1) or float ([0, 1]), default=None Minimum...stop_probability float in range [0, 1], default=0.99 RANSAC iteration stops...scikit-learn.org/stable/modules/generated/sklearn.linear_model.RANSACRegressor.html -
TweedieRegressor — scikit-learn 1.8.0 documenta...
power = 0.0 , alpha = 1.0 , fit_intercept = True...max_iter = 100 , tol = 0.0001 , warm_start = False , verbose = 0 ) [source]...scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html -
BiclusterMixin — scikit-learn 1.8.0 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7 ],...get_indices ( 0 ) (array([0, 1, 2, 3, 4, 5]), array([0, 1])) get_indices...scikit-learn.org/stable/modules/generated/sklearn.base.BiclusterMixin.html -
l1_min_c — scikit-learn 1.8.0 documentation
intercept_scaling = 1.0 ) [source] # Return the lowest...intercept_scaling float, default=1.0 When fit_intercept is True, instance...scikit-learn.org/stable/modules/generated/sklearn.svm.l1_min_c.html -
sklearn — scikit-learn 1.8.0 documentation
Configure global settings and get information about the working environment.scikit-learn.org/stable/api/sklearn.html -
governance.rst.txt
page: Requires +1 by a core contributor, no -1 by a core contributor...changes** require +1 by two core contributors, no -1 by a core contributor...scikit-learn.org/stable/_sources/governance.rst.txt -
plot_classifier_comparison.ipynb
\n GaussianProcessClass(1.0 * RBF(1.0), random_state=42),\n D...0].min() - 0.5, X[:, 0].max() + 0.5\n y_min, y_max = X[:, 1].min()...scikit-learn.org/stable/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb -
MiniBatchKMeans — scikit-learn 1.8.0 documentation
([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2 ], [ 4 , 0 ], [...2.48979592], [1.06896552, 1. ]]) >>> kmeans . predict ([[ 0 , 0 ], [ 4...scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html