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Neighborhood Components Analysis Illustration —...
n_clusters_per_class = 1 , class_sep = 1.0 , random_state = 0 ,...], pt_j [ 0 ]], [ pt_i [ 1 ], pt_j [ 1 ]]) ax . plot ( * line ,...scikit-learn.org/stable/auto_examples/neighbors/plot_nca_illustration.html -
SGD: convex loss functions — scikit-learn 1.8.0...
z >= - 1 ] = ( 1 - z [ z >= - 1 ]) ** 2 loss [ z >= 1.0 ] = 0...([ xmin , 0 , 0 , xmax ], [ 1 , 1 , 0 , 0 ], color = "gold" ,...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html -
Gaussian Mixture Model Selection — scikit-learn...
1 ], [ 1.7 , 0.4 ]]) component_1 =...n_samples , 2 ) + np . array ([ - 4 , 1 ]) # spherical X = np . concatenate...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_selection.html -
Contributing — scikit-learn 1.8.0 documentation
setting SKLEARN_RUN_FLOAT32_TESTS=1 . See Environment variables for...environment variable NO_MATHJAX=1 if you intend to view the documentation...scikit-learn.org/stable/developers/contributing.html -
SGD: Maximum margin separating hyperplane — sci...
linspace ( - 1 , 5 , 10 ) yy = np . linspace ( - 1 , 5 , 10 ) X1...= p [ 0 ] levels = [ - 1.0 , 0.0 , 1.0 ] linestyles = [ "dashed"...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html -
Demonstrating the different strategies of KBins...
strategies ) + 1 , i ) ax . scatter ( X [:, 0 ], X [:, 1 ], edgecolors.... linspace ( X [:, 1 ] . min (), X [:, 1 ] . max (), 300 ), )...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html -
Robust vs Empirical covariance estimate — sciki...
5 )[ 1 : - 1 ], ) ) . astype ( int ) # definition...= 11 ) plt . subplot ( 2 , 1 , 1 ) lw = 2 plt . errorbar ( range_n_outliers...scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.html -
Visualizing the stock market structure — scikit...
index ] = 1 dy = y - embedding [ 1 ] dy [ index ] = 1 this_dx =...alphas = np . logspace ( - 1.5 , 1 , num = 10 ) edge_model = covariance...scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html -
4. Metadata Routing — scikit-learn 1.8.0 docume...
1.1. Weighted scoring and fitting...>>> param_grid = { "C" : [ 0.1 , 1 ]} >>> lr = LogisticRegression...scikit-learn.org/stable/metadata_routing.html -
Label Propagation digits: Active learning — sci...
support 0 1.00 1.00 1.00 22 1 0.85 1.00 0.92 22 2 1.00 1.00 1.00 28...support 0 1.00 1.00 1.00 22 1 0.85 1.00 0.92 22 2 1.00 1.00 1.00 27...scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learni...