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Results tagged with precision-recall
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user 361202
P&R are a way to measure the relevance of set of retrieved instances. Precision is the % of correct instances out of all instances retrieved. Relevance is the % of true instances retrieved. The harmonic mean of P&R is the F1-score. P&R are used in data mining to evaluate classifiers.
1
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Why although my precision for class 1 is very low (0.05), and the recall is 0.92, the Precis...
PR curve plot displays metrics for every possible classification threshold. If you did not resample your data, the optimal threshold will be far from the default 0.5 used by predict() or classificatio …
- 351
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How to get the threshold from PrecisionRecallDisplay?
PrecisionRecallDisplay() is just a very basic wrapper for sklearn.metrics.precision_recall_curve(). The latter returns numpy arrays for precision, recall and thresholds, which allow easy vectorized ha …
- 351