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AgglomerativeClustering — scikit-learn 1....
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2...clustering . labels_ array([1, 1, 1, 0, 0, 0]) For a comparison...scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html -
Lasso — scikit-learn 1.8.0 documentation
1 ) >>> clf . fit ([[ 0 , 0 ], [ 1 , 1 ], [ 2...2 , 2 ]], [ 0 , 1 , 2 ]) Lasso(alpha=0.1) >>> print (...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html -
LinearRegression — scikit-learn 1.8.0 doc...
array ([[ 1 , 1 ], [ 1 , 2 ], [ 2 , 2 ], [ 2 ,...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html -
make_blobs — scikit-learn 1.8.0 documenta...
scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Plot the...>>> y array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0]) >>> X ,...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html -
precision_recall_curve — scikit-learn 1.8...
y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise...0.66666667, 0.5 , 1. , 1. ]) >>> recall array([1. , 1. , 0.5,...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html -
make_friedman3 — scikit-learn 1.8.0 docum...
1 ] <= 560 * pi , 0 <= X [:, 2 ] <= 1 , 1 <=...arctan (( X [:, 1 ] * X [:, 2 ] - 1 / ( X [:, 1 ] * X [:, 3 ]))...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman3.html -
make_column_selector — scikit-learn 1.8.0...
1. , 0. , 0. ], [-1.50755672, 1. , 0. , 0. ], [-0.30151134,...[-0.30151134, 0. , 1. , 0. ], [ 0.90453403, 0. , 0. , 1. ]]) __call__...scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html -
class_likelihood_ratios — scikit-learn 1....
1 , 0 , 1 , 0 ], [ 1 , 1 , 0 , 0 , 0 ]) (1.5, 0.75)...LR+ ranges from 1.0 to infinity. A LR+ of 1.0 indicates that...scikit-learn.org/stable/modules/generated/sklearn.metrics.class_likelihood_ratios.html -
lars_path_gram — scikit-learn 1.8.0 docum...
the case method=’lasso’ is: ( 1 / ( 2 * n_samples )) * || y -...equation (see discussion in [1] ). Read more in the User Guide...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lars_path_gram.html -
sigmoid_kernel — scikit-learn 1.8.0 docum...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0...defaults to 1.0 / n_features. coef0 float, default=1 Constant offset...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html