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Robust linear model estimation using RANSAC — s...
linear_model n_samples = 1000 n_outliers = 50 X , y , coef = datasets . make_regression...n_samples = n_samples , n_features = 1 , n_informative = 1 , noise...scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html -
SGDRegressor — scikit-learn 1.7.1 documentation
tol = 0.001 , shuffle = True , verbose = 0 , epsilon = 0.1 , random_state...( loss = 'squared_error' , * , penalty = 'l2' , alpha = 0.0001...scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html -
johnson_lindenstrauss_min_dim — scikit-learn 1....
dtype=float, default=0.1 Maximum distortion...in_dim ( n_samples , * , eps = 0.1 ) [source] # Find a ‘safe’...scikit-learn.org/stable/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim... -
Demonstration of multi-metric evaluation on cro...
alpha = 0.1 if sample == "test" else 0 , color = color , )...style , color = color , alpha = 1 if sample == "test" else 0.7...scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html -
LocallyLinearEmbedding — scikit-learn 1.7.1 doc...
n_neighbors = 5 , n_components = 2 , reg = 0.001 , eigen_solver = 'auto'...'auto' , tol = 1e-06 , max_iter = 100 , method = 'standard' , hessian_tol...scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html -
MNIST classification using multinomial logistic...
sparsity = np . mean ( clf . coef_ == 0 ) * 100 score = clf . score...return_X_y = True , as_frame = False ) random_state = check_random_state...scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html -
column_or_1d — scikit-learn 1.7.1 documentation
dtype = None , warn = False , device = None ) [source]...data. dtype data-type, default=None Data type for y . Added in...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.column_or_1d.html -
Release Highlights for scikit-learn 1.3 — sciki...
random_state = rng ) coef = rng . uniform ( low =- 10 , high = 20 , size...size = n_features ) y = rng . gamma ( shape = 2 , scale = np ....scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_3_0.html -
DecisionBoundaryDisplay — scikit-learn 1.7.1 do...
multiclass_colors = None , xlabel = None , ylabel = None , ax = None , **...plot_method = 'contourf' , ax = None , xlabel = None , ylabel = None...scikit-learn.org/stable/modules/generated/sklearn.inspection.DecisionBoundaryDisplay.html -
enet_path — scikit-learn 1.7.1 documentation
l1_ratio = 0.5 , eps = 0.001 , n_alphas = 100 , alphas = None ,...precompute = 'auto' , Xy = None , copy_X = True , coef_init = None...scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html