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ExtraTreeRegressor — scikit-learn 1.7.2 documen...
criterion = 'squared_error' , splitter = 'random' , max_depth = None...min_weight_fraction_leaf = 0.0 , max_features = 1.0 , random_state = None , m...scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeRegressor.html -
HistGradientBoostingClassifier — scikit-learn 1...
n_iter_no_change = 10 , tol = 1e-07 , verbose = 0 , random_state = None ,...loss = 'log_loss' , * , learning_rate = 0.1 , max_iter = 100 ,...scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html -
Joint feature selection with multi-task Lasso —...
n_tasks = 100 , 30 , 40 n_relevant_features = 5 coef = np . zeros...feature_to_plot ], color = "seagreen" , linewidth = lw , label = "Ground truth"...scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html -
validation_curve — scikit-learn 1.7.2 documenta...
groups = None , cv = None , scoring = None , n_jobs = None ,...pre_dispatch = 'all' , verbose = 0 , error_score = nan , fit_params...scikit-learn.org/stable/modules/generated/sklearn.model_selection.validation_curve.html -
recall_score — scikit-learn 1.7.2 documentation
labels = None , pos_label = 1 , average = 'binary' , sample_weight...sample_weight = None , zero_division = 'warn' ) [source] # Compute...scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html -
Early stopping in Gradient Boosting — scikit-le...
params = dict ( n_estimators = 1000 , max_depth = 5 , learning_rate...learning_rate = 0.1 , random_state = 42 ) gbm_full = GradientBoostingRegr...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html -
GaussianProcessRegressor — scikit-learn 1.7.2 d...
( kernel = None , * , alpha = 1e-10 , optimizer = 'fmin_l_bfgs_b'...n_restarts_optimizer = 0 , normalize_y = False , copy_X_train = True , n_targets...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html -
3.4. Metrics and scoring: quantifying the quali...
y = datasets . load_iris ( return_X_y = True ) >>> clf = svm...greater_is_better = False ) >>> X = [[ 1 ], [ 1 ]] >>> y = [ 0 , 1 ]...scikit-learn.org/stable/modules/model_evaluation.html -
cross_validate — scikit-learn 1.7.2 documentation
y = None , * , groups = None , scoring = None , cv = None...n_jobs = None , verbose = 0 , params = None , pre_dispatch = '2*n_jobs'...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html -
make_blobs — scikit-learn 1.7.2 documentation
( n_samples = 100 , n_features = 2 , * , centers = None , cluster_std...cluster_std = 1.0 , center_box = (-10.0, 10.0) , shuffle = True ,...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html