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grid_to_graph — scikit-learn 1.6.1 documentation
n_z=1 , * , mask=None , return_as=<class 'scipy.s...shape_img = ( 4 , 4 , 1 ) >>> mask = np . zeros ( shape = shape_img...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.grid_to_graph.html -
make_friedman3 — scikit-learn 1.6.1 documentation
intervals: 0 <= X [:, 0 ] <= 100 , 40 * pi <= X [:, 1 ] <= 560 * pi...pi , 0 <= X [:, 2 ] <= 1 , 1 <= X [:, 3 ] <= 11. The output y...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman3.html -
A demo of structured Ward hierarchical clusteri...
contour ( label == l , colors = [ plt . cm . nipy_spectral...smoothened_coins = gaussian_filter ( orig_coins , sigma = 2 ) rescaled_coins...scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html -
fetch_lfw_people — scikit-learn 1.6.1 documenta...
download_if_missing = True , return_X_y = False , n_retries = 3 , delay = 1.0..., data_home = None , funneled = True , resize = 0.5 , min_faces_per_person...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html -
Lasso model selection via information criteria ...
y = load_diabetes ( return_X_y = True , as_frame = True )...aic_criterion , color = "tab:blue" , marker = "o" , label = "AIC criterion"...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html -
RBFSampler — scikit-learn 1.6.1 documentation
gamma = 1.0 , n_components = 100 , random_state = None ) [source]...rbf_feature = RBFSampler ( gamma = 1 , random_state = 1 ) >>> X_features...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html -
ExtraTreeClassifier — scikit-learn 1.6.1 docume...
criterion = 'gini' , splitter = 'random' , max_depth = None , min_samples_split...min_weight_fraction_leaf = 0.0 , max_features = 'sqrt' , random_state = None ,...scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html -
Release Highlights for scikit-learn 1.0 — sciki...
n_iter_no_change = 10 , tol = 1e-7 , verbose = 0 , random_state = None ,...loss = "squared_error" , learning_rate = 0.1 , max_iter = 100...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html -
TweedieRegressor — scikit-learn 1.6.1 documenta...
power = 0.0 , alpha = 1.0 , fit_intercept = True , link = 'auto'...'auto' , solver = 'lbfgs' , max_iter = 100 , tol = 0.0001 , warm_start...scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html -
HuberRegressor — scikit-learn 1.6.1 documentation
n_samples = 200 , n_features = 2 , noise = 4.0 , coef = True , random_state...( * , epsilon = 1.35 , max_iter = 100 , alpha = 0.0001 , warm_start...scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html