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PoissonRegressor — scikit-learn 1.6.1 documenta...
max_iter = 100 , tol = 0.0001 , warm_start = False , verbose = 0 )...* , alpha = 1.0 , fit_intercept = True , solver = 'lbfgs' , max_iter...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html -
GaussianMixture — scikit-learn 1.6.1 documentation
reg_covar = 1e-06 , max_iter = 100 , n_init = 1 , init_params = 'kmeans'...n_components = 1 , * , covariance_type = 'full' , tol = 0.001 , reg_covar...scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.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 -
Label Propagation digits active learning — scik...
bottom = 0.03 , right = 0.9 , top = 0.9 , wspace = 0.2 , hspace...n_total_samples = len ( y ) n_labeled_points = 40 max_iterations = 5 unlabeled_indices...scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learni... -
TfidfTransformer — scikit-learn 1.6.1 documenta...
norm = 'l2' , use_idf = True , smooth_idf = True , sublinear_tf...as idf(t) = log [ n / df(t) ] + 1 (if smooth_idf=False ), where...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.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 -
Hashing feature transformation using Totally Ra...
y = make_circles ( factor = 0.5 , random_state = 0 , noise...random_state = 0 , max_depth = 3 ) X_transformed = hasher . fit_transform...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html -
orthogonal_mp — scikit-learn 1.6.1 documentation
n_nonzero_coefs = None , tol = None , precompute = False , copy_X = True...>>> X , y = make_regression ( noise = 4 , random_state = 0 ) >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp.html -
Permutation Importance with Multicollinear or C...
random_state = 42 , n_jobs = 2 ) perm_sorted_idx = result . importances_mean..., y = load_breast_cancer ( return_X_y = True , as_frame = True...scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance_multicollinear.html