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completeness_score — scikit-learn 1.7.2 d...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect labelings...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
brier_score_loss — scikit-learn 1.7.2 doc...
y_true in {-1, 1} or {0, 1}, pos_label defaults to 1; else if y_true...defined as: \[\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}(y_{ic} - \hat{p}_{ic})^{2}\]...scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html -
LabelBinarizer — scikit-learn 1.7.2 docum...
array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0]]) fit ( y )...fit ( np . array ([[ 0 , 1 , 1 ], [ 1 , 0 , 0 ]])) LabelBinarizer()...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html -
precision_score — scikit-learn 1.7.2 docu...
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred...= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html -
NearestNeighbors — scikit-learn 1.7.2 doc...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) radius_neighbors...() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) set_params...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
minmax_scale — scikit-learn 1.7.2 documen...
1 , 2 ], [ - 1 , 0 , 1 ]] >>> minmax_scale...independently array([[0., 1., 1.], [1., 0., 0.]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html -
f1_score — scikit-learn 1.7.2 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred...= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html -
confusion_matrix — scikit-learn 1.7.2 doc...
1 , 0 , 1 ], [ 1 , 1 , 1 , 0 ]) . ravel ()...) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) >>> y_true...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html -
make_sparse_spd_matrix — scikit-learn 1.7...
array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0.,...elements all 1. smallest_coef float, default=0.1 The value of...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html -
KNeighborsRegressor — scikit-learn 1.7.2 ...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) predict...], [ 1 ], [ 2 ], [ 3 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html