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KBinsDiscretizer — scikit-learn 1.7.2 doc...
[ 2., 2., 2., 1.], [ 2., 2., 2., 2.]]) Sometimes...>>> X = [[ - 2 , 1 , - 4 , - 1 ], ... [ - 1 , 2 , - 3 , - 0.5...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html -
PoissonRegressor — scikit-learn 1.7.2 doc...
determination R^2. R^2 uses squared error and D^2 uses the deviance...() >>> X = [[ 1 , 2 ], [ 2 , 3 ], [ 3 , 4 ], [ 4 , 3...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html -
make_classification — scikit-learn 1.7.2 ...
n_informative = 2 , n_redundant = 2 , n_repeated = 0 , n_classes = 2 , n_clusters_per_class...n_clusters_per_class = 2 , weights = None , flip_y = 0.01 , class_sep...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html -
brier_score_loss — scikit-learn 1.7.2 doc...
2 , 0.7 , 0.1 ], [ 0.2 , 0.2 , 0.6 ]], ... labels...by 1/2 to lie in the [0, 1] range instead of the [0, 2] range....scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html -
enet_path — scikit-learn 1.7.2 documentation
is: ( 1 / ( 2 * n_samples )) * || Y - XW || _Fro ^ 2 + alpha *...mono-output tasks it is: 1 / ( 2 * n_samples ) * || y - Xw ||^...scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html -
NearestNeighbors — scikit-learn 1.7.2 doc...
2 , return_distance = False ) array([[2, 0]]...) >>>...array([[2]])) As you can see, it returns [[0.5]], and [[2]], which...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
NearestCentroid — scikit-learn 1.7.2 docu...
[ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2 , 1 ], [ 3...3 , 2 ]]) >>> y = np . array ([ 1 , 1 , 1 , 2 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html -
mean_absolute_error — scikit-learn 1.7.2 ...
2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>...>>> y_pred = [[ 0 , 2 ], [ - 1 , 2 ], [ 8 , - 5 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html -
mean_squared_error — scikit-learn 1.7.2 d...
2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>...>>> y_pred = [[ 0 , 2 ],[ - 1 , 2 ],[ 8 , - 5 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html -
accuracy_score — scikit-learn 1.7.2 docum...
2 , 1 , 3 ] >>> y_true = [ 0 , 1 , 2 , 3 ] >>>...], [ 1 , 1 ]]), np . ones (( 2 , 2 ))) 0.5 Gallery examples # Plot...scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html