F Beta Score begingroup I completely share your concerns about proper scoring rules but it is possible to derive quadratic errors analogously to Brier s score that capture certain aspects of predictive behaviour like sensitivity specificity or the predictive values and in consequence also an analogoue to the F measures
The formula for F measure F1 with beta 1 is the same as the formula giving the equivalent resistance composed of two resistances placed in parallel in physics forgetting about the factor 2 This could give you a possible interpretation and you can think about both electronic or thermal resistances Wikipedia defines F1 Score or F Score as the harmonic mean of precision and recall But aren t Precision and Recall found only when the result of predicted values of a logistic regression for example is transformed to binary using a cutoff Now by cutoff I remember what is the connection between F1 Score and Optimal Threshold
F Beta Score
F Beta Score
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F beta score s formula calculates like this F beta 1 beta 2 frac PR beta 2P R However according to some sources in case I want to add more emphasis to Precision I should use beta 1 and complementary in case I want to add less emphasis to Precision than Recall I should use beta 1 I m using sklearn s confusion matrix and classification report methods to compute the confusion matrix and F1 Score of a simple multiclass classification project I m doing For some classes the F1 Score that I m getting is higher than the accuracy and this seems strange to me Is it possible or am I doing something wrong
I am comparing a ML classifier to a bunch of other benchmark F1 classifiers by F1 scores By AUPRC my classifier does worse than other benchmark methods When I compared F1 score however I got a The Brier score is a function of probabilistic classifications and the confusion matrix is can be a function of these probabilistic classifications plus a threshold Thus the Brier or log score is not just a function of the confusion matrix or of accuracy
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begingroup what happens when beta goes past one like 2 making it F2 score or more will more weight be given to precision endgroup Naveen Reddy Marthala Commented Aug 28 2020 at 7 32 I actually think that AUPRC is a good way to go it essentially measures precision as a function of recall at varying thresholds but since you ve mentioned that already there s one more thing you can consider the F beta measure
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Confusion Matrix Accuracy Precision Recall F1 Score Yarak001
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https://stats.stackexchange.com/questions/451677/what-are-best-practi…
begingroup I completely share your concerns about proper scoring rules but it is possible to derive quadratic errors analogously to Brier s score that capture certain aspects of predictive behaviour like sensitivity specificity or the predictive values and in consequence also an analogoue to the F measures
https://stats.stackexchange.com/questions/49226
The formula for F measure F1 with beta 1 is the same as the formula giving the equivalent resistance composed of two resistances placed in parallel in physics forgetting about the factor 2 This could give you a possible interpretation and you can think about both electronic or thermal resistances
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F Beta Score - I am comparing a ML classifier to a bunch of other benchmark F1 classifiers by F1 scores By AUPRC my classifier does worse than other benchmark methods When I compared F1 score however I got a
