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Plot class probabilities calculated by the Voti...
random_state = 123 ) clf2 = RandomForestClassifi ( n_estimators = 100..., random_state = 123 ) clf3 = GaussianNB () X = np . array ([[...scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html -
BisectingKMeans — scikit-learn 1.6.1 documentation
random_state = None , max_iter = 300 , verbose = 0 , tol = 0.0001 ,...( n_clusters = 8 , * , init = 'random' , n_init = 1 , random_state...scikit-learn.org/stable/modules/generated/sklearn.cluster.BisectingKMeans.html -
Hyperparameter — scikit-learn 1.6.1 documentation
y = make_friedman2 ( n_samples = 50 , noise = 0 , random_state...random_state = 0 ) >>> kernel = ConstantKernel ( constant_value = 1.0...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html -
Effect of model regularization on training and ...
n_informative = 50 , shuffle = False , noise = 1.0 , coef = True , random_state...color = "k" , linewidth = 2 , linestyle = "--" , label = f "Optimum...scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.html -
BayesianRidge — scikit-learn 1.6.1 documentation
max_iter = 300 , tol = 0.001 , alpha_1 = 1e-06 , alpha_2 = 1e-06...lambda_1 = 1e-06 , lambda_2 = 1e-06 , alpha_init = None , lambda_init...scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html -
Gaussian Mixture Model Ellipsoids — scikit-lear...
any ( Y_ == i ): continue plt . scatter ( X [ Y_ == i , 0 ],...], X [ Y_ == i , 1 ], 0.8 , color = color ) # Plot an ellipse to...scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html -
RadiusNeighborsTransformer — scikit-learn 1.6.1...
leaf_size = 30 , metric = 'minkowski' , p = 2 , metric_params = None...* , mode = 'distance' , radius = 1.0 , algorithm = 'auto' , leaf_size...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsTransformer.html -
non_negative_factorization — scikit-learn 1.6.1...
W = None , H = None , n_components = 'auto' , * , init = None...tol = 0.0001 , max_iter = 200 , alpha_W = 0.0 , alpha_H = 'same'...scikit-learn.org/stable/modules/generated/sklearn.decomposition.non_negative_factorization.html -
RandomTreesEmbedding — scikit-learn 1.6.1 docum...
n_estimators = 100 , * , max_depth = 5 , min_samples_split = 2 , min_samples_leaf...max_leaf_nodes = None , min_impurity_decrease = 0.0 , sparse_output = True...scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomTreesEmbedding.html -
6.4. Imputation of missing values — scikit-lear...
>>> imp = SimpleImputer ( missing_values =- 1 , strategy = 'mean'...imp = IterativeImputer ( max_iter = 10 , random_state = 0 ) >>>...scikit-learn.org/stable/modules/impute.html