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1.16. Probability calibration — scikit-learn 1....
ensemble=False . The main advantage of using ensemble=False is...logistic model [ 4 ] : \[p(y_i = 1 | f_i) = \frac{1}{1 + \exp(A f_i...scikit-learn.org/stable/modules/calibration.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 -
zero_one_loss — scikit-learn 1.6.1 documentation
normalize = True , sample_weight = None ) [source] #...loss float or int, If normalize == True , return the fraction of...scikit-learn.org/stable/modules/generated/sklearn.metrics.zero_one_loss.html -
label_binarize — scikit-learn 1.6.1 documentation
neg_label = 0 , pos_label = 1 , sparse_output = False ) [source]...class. neg_label int, default=0 Value with which negative labels...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.label_binarize.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 -
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 -
Version 1.3 — scikit-learn 1.6.1 documentation
data types when as_frame=True and parser="liac-arff" . #26386 by...gression with penalty="l1" and solver="liblinear" on linearly...scikit-learn.org/stable/whats_new/v1.3.html -
Feature importances with a forest of trees — sc...
n_repeated = 0 , n_classes = 2 , random_state = 0 , shuffle = False ,...n_repeats = 10 , random_state = 42 , n_jobs = 2 ) elapsed_time = time...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html