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sigmoid_kernel — scikit-learn 1.8.0 documentation
X = [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1...1 , 1 , 0 ]] >>> sigmoid_kernel ( X , Y ) array([[0.76, 0.76],...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html -
pairwise_distances_argmin_min — scikit-learn 1....
X = [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1...argmin array([0, 1]) >>> distances array([1., 1.]) On this page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html -
sklearn.metrics — scikit-learn 1.8.0 documentation
Score functions, performance metrics, pairwise metrics and distance computations. User guide. See the Metrics and scoring: quantifying the quality of predictions and Pairwise metrics, Affinities an...scikit-learn.org/stable/api/sklearn.metrics.html -
DetCurveDisplay — scikit-learn 1.8.0 documentation
y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise...version 1.8: y_pred is deprecated and will be removed in 1.10. Use...scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html -
fbeta_score — scikit-learn 1.8.0 documentation
= [ 0 , 1 , 2 , 0 , 1 , 2 ] >>> y_pred = [ 0 , 2 , 1 , 0 , 0...beta = 0.5 ) array([0.71, 0. , 0. ]) >>> y_pred_empty = [ 0 , 0...scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html -
ndcg_score — scikit-learn 1.8.0 documentation
scores ) 0.69 >>> scores = np . asarray ([[ .05 , 1.1 , 1. , .5...scores = np . asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html -
orthogonal_mp_gram — scikit-learn 1.8.0 documen...
shape (100,) >>> X [: 1 ,] @ coef array([-78.68])...default) this value is set to 10% of n_features. tol float, default=None...scikit-learn.org/stable/modules/generated/sklearn.linear_model.orthogonal_mp_gram.html -
normalized_mutual_info_score — scikit-learn 1.8...
_score ([ 0 , 0 , 1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> normal..._score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 If classes...scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html -
pair_confusion_matrix — scikit-learn 1.8.0 docu...
([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) array([[8, 0], [0, 4]]......([ 0 , 0 , 1 , 2 ], [ 0 , 0 , 1 , 1 ]) array([[8, 2], [0, 2]]......scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html -
d2_brier_score — scikit-learn 1.8.0 documentation
y_true in {-1, 1} or {0, 1}, pos_label defaults to 1; else if y_true...explained. Best possible score is 1.0 and it can be negative because...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_brier_score.html