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Adjustment for chance in clustering performance...
linspace ( 2 , n_samples , 10 ) . astype ( int ) plt . figure ( 2 ) plots...n_clusters_range = np . linspace ( 2 , 100 , 10 ) . astype ( int )...scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html -
homogeneity_score — scikit-learn 1.8.0 do...
2 ])) 1.000000 >>> print...([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 2 , 3 ])) 1.000000 Clusters that...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
LassoLars — scikit-learn 1.8.0 documentation
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...max_iter = 500 , eps = np.float64(2.220446049250313e-16) , copy_X...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLars.html -
RepeatedKFold — scikit-learn 1.8.0 docume...
2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]]) >>>...RepeatedKFold ( n_splits = 2 , n_repeats = 2 , random_state = 2652124...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html -
3.1. Cross-validation: evaluating estimator per...
2 , 2 , 2 , 2 ] >>> groups = [ 1 , 1 , 2 , 2 , 3...)) [2 3] [0 1] [1 3] [0 2] [1 2] [0 3] [0 3] [1 2] [0 2] [1 3]...scikit-learn.org/stable/modules/cross_validation.html -
Lars — scikit-learn 1.8.0 documentation
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...n_nonzero_coefs = 500 , eps = np.float64(2.220446049250313e-16) , copy_X...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html -
SGD: convex loss functions — scikit-learn...
= ( 1 - z [ z >= - 1 ]) ** 2 loss [ z >= 1.0 ] = 0 return...linspace ( xmin , xmax , 100 ) lw = 2 plt . plot ([ xmin , 0 , 0 , xmax...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html -
make_circles — scikit-learn 1.8.0 documen...
n_samples int or tuple of shape (2,), dtype=int, default=100 If int,...ndarray of shape (n_samples, 2) The generated samples. y ndarray...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html -
safe_sqr — scikit-learn 1.8.0 documentation
Returns : X ** 2 element wise square Return the...>>> safe_sqr ([ 1 , 2 , 3 ]) array([1, 4, 9]) On this...scikit-learn.org/stable/modules/generated/sklearn.utils.safe_sqr.html -
LeavePOut — scikit-learn 1.8.0 documentation
3] Test: index=[0 2] Fold 2: Train: index=[1 2] Test: index=[0...([ 1 , 2 , 3 , 4 ]) >>> lpo = LeavePOut ( 2 ) >>>...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html