Comments (1)
liliaevgeniou
commented on June 10, 2024
I'm sorry, I figured out why this didn't work.
MLmetrics assumes "positive" is 0, and "negative" is 1. Hence one must specify that the positives are 1, or otherwise do 1-y_true and 1-y_pred.
MLmetrics::F1_Score(y_true = y_true, y_pred = y_pred, positive = 1)
or
MLmetrics::F1_Score(y_true = 1-y_true, y_pred = 1-y_pred)
from mlmetrics.
Related Issues (11)
- typo ''Possion" instead of "Poisson" HOT 2
- Inconsistencies and errors with `Precision()`
- Integer Overflow in AUC HOT 1
- R2_Score() is inconsistent with other methods HOT 1
- Support for multi-class classification metrics such as micro/macro f1-score, precision and recall
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- Inconsistent order of y_true and y_pred arguments
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- Wrong logloss if `y_pred` is not a matrix and does not contain all the possible classes
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from mlmetrics.