Here is a very small example using precision_recall_curve(): from sklearn.metrics import precision_recall_curve, precision_score, recall_score y_true = [0, 1] y_predict_proba = [0.25,0.75] precision, recall, thresholds = precision_recall_curve(y_true, y_predict_proba) precision, recall
which results in: (array([1., 1.]), array([1., 0.])) Now let's calculate manually to see whether that's correct. There are three possible class vectors depending on threshold: [0,0], [0,1], and [1,1]. We have to discard [0,0] because it gives an undefined precision (divide by zero). So, applying precision_score() and recall_score() to the other two: y_predict_class=[0,1] precision_score(y_true, y_predict_class), recall_score(y_true, y_predict_class) which gives: (1.0, 1.0) and y_predict_class=[1,1] precision_score(y_true, y_predict_class), recall_score(y_true, y_predict_class) which gives (0.5, 1.0) This seems not to match the output of precision_recall_curve() (which for example did not produce a 0.5 precision value). Am I missing something?
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