sklearn.metrics.auc
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sklearn.metrics.auc(x, y)
[source] -
Compute Area Under the Curve (AUC) using the trapezoidal rule.
This is a general function, given points on a curve. For computing the area under the ROC-curve, see
roc_auc_score
. For an alternative way to summarize a precision-recall curve, seeaverage_precision_score
.- Parameters
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xndarray of shape (n,)
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x coordinates. These must be either monotonic increasing or monotonic decreasing.
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yndarray of shape, (n,)
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y coordinates.
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- Returns
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aucfloat
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See also
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roc_auc_score
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Compute the area under the ROC curve.
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average_precision_score
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Compute average precision from prediction scores.
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precision_recall_curve
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Compute precision-recall pairs for different probability thresholds.
Examples
>>> import numpy as np >>> from sklearn import metrics >>> y = np.array([1, 1, 2, 2]) >>> pred = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2) >>> metrics.auc(fpr, tpr) 0.75
Examples using sklearn.metrics.auc
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Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.metrics.auc.html