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lasso_path — scikit-learn 1.6.1 documentation
alpha * || w || _1 For multi-output tasks it is: ( 1 / ( 2 * n_samples...lasso_path >>> X = np . array ([[ 1 , 2 , 3.1 ], [ 2.3 , 5.4 , 4.3 ]])...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html -
affinity_propagation — scikit-learn 1.6.1 docum...
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2...>>> labels array([0, 0, 0, 1, 1, 1]) Gallery examples # Visualizing...scikit-learn.org/stable/modules/generated/sklearn.cluster.affinity_propagation.html -
ExpSineSquared — scikit-learn 1.6.1 documentation
ExpSineSquared ( length_scale = 1.0 , periodicity = 1.0 , length_scale_bounds...ExpSineSquared ( length_scale = 1 , periodicity = 1 ) >>> gpr = GaussianProcessRegre...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html -
1.10. Decision Trees — scikit-learn 1.6.1 docum...
1 , 2 , 3 ]) . reshape ( - 1 , 1 ) >>> y = [ 0 , 1 , 1 , 1.... nan , - 1 , np . nan , 1 ]) . reshape ( - 1 , 1 ) >>> y = [...scikit-learn.org/stable/modules/tree.html -
1.6. Nearest Neighbors — scikit-learn 1.6.1 doc...
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...scikit-learn.org/stable/modules/neighbors.html -
make_blobs — scikit-learn 1.6.1 documentation
scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Release Highlights...2) >>> y array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0]) >>> X , y = make_blobs...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html -
RBF — scikit-learn 1.6.1 documentation
return_X_y = True ) >>> kernel = 1.0 * RBF ( 1.0 ) >>> gpc = GaussianProcessClass...ernels. RBF ( length_scale = 1.0 , length_scale_bounds = (1e-05,...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html -
KernelDensity — scikit-learn 1.6.1 documentation
log_density array([-1.52955942, -1.51462041, -1.60244657]) fit (...KernelDensity ( * , bandwidth = 1.0 , algorithm = 'auto' , kernel...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html -
1.16. Probability calibration — scikit-learn 1....
model [ 4 ] : \[p(y_i = 1 | f_i) = \frac{1}{1 + \exp(A f_i + B)} \,,\]...e.g. using many more features. 1.16.1. Calibration curves # Calibration...scikit-learn.org/stable/modules/calibration.html -
silhouette_samples — scikit-learn 1.6.1 documen...
The best value is 1 and the worst value is -1. Values near 0 indicate...2 <= n_labels <= n_samples - 1 . This function returns the Silhouette...scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_samples.html