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CountVectorizer — scikit-learn 1.8.0 docu...
[[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0...[[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html -
completeness_score — scikit-learn 1.8.0 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 1.8.0 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 -
sparse_encode — scikit-learn 1.8.0 docume...
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.decomposition.sparse_encode.html -
LabelBinarizer — scikit-learn 1.8.0 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 1.8.0 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 1.8.0 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 -
polynomial_kernel — scikit-learn 1.8.0 do...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0..., degree = 2 ) array([[1. , 1. ], [1.77, 2.77]]) On this page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html -
PredefinedSplit — scikit-learn 1.8.0 docu...
1 , 1 ]) >>> test_fold = [ 0 , 1 , - 1 , 1 ] >>>...PredefinedSplit(test_fold=array([ 0, 1, -1, 1])) >>> for i , (...scikit-learn.org/stable/modules/generated/sklearn.model_selection.PredefinedSplit.html -
paired_manhattan_distances — scikit-learn...
array ([[ 1 , 1 , 0 ], [ 0 , 1 , 0 ], [ 0 , 0 , 1 ]]) >>>...calculated between (X[0], Y[0]), (X[1], Y[1]), …, (X[n_samples], Y[n_samples])....scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html