operdeck
commented
9 months ago Just remember the ADM models do not predict 0/1 but a probability.
Op di 7 nov. 2023 16:08 schreef Stijn Kas @.***>:
I have always used metrics such as Precision, Recall and F-Score in conjunction with AUC score. I think these metrics are important to consider if there is class imbalance. Precision, recall, and F-score provide more specific information regarding how well the model performs in correctly identifying a specific class. AUC, while helpful in measuring the model's ability to distinguish between classes, does not provide class-specific details.
Since they are already included in R and my reasoning above I hope this provides a valid use case to include them in the python scripts as well. :)
Ideally these are the type of stats I would like to see to compare model performance when we perform experimentation: [image: f217464c-6b13-4bbf-9a12-28173bcf1f6c] https://user-images.githubusercontent.com/69606569/279380123-308a4f6c-7c6a-4826-b789-bb843da2c2aa.jpg
*Originally posted by @sushantkhare https://github.com/sushantkhare in
153 (reply in thread)
https://github.com/pegasystems/pega-datascientist-tools/discussions/153#discussioncomment-7434953*
— Reply to this email directly, view it on GitHub https://github.com/pegasystems/pega-datascientist-tools/issues/165, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABEVTKET7FHLUCFV2U3KZ5LYDJFF5AVCNFSM6AAAAAA7BLCUBCVHI2DSMVQWIX3LMV43ASLTON2WKOZRHE4DCNJYGYZTIMQ . You are receiving this because you are subscribed to this thread.Message ID: @.***>
StijnKas
I have always used metrics such as Precision, Recall and F-Score in conjunction with AUC score. I think these metrics are important to consider if there is class imbalance. Precision, recall, and F-score provide more specific information regarding how well the model performs in correctly identifying a specific class. AUC, while helpful in measuring the model's ability to distinguish between classes, does not provide class-specific details.
Since they are already included in R and my reasoning above I hope this provides a valid use case to include them in the python scripts as well. :)
Ideally these are the type of stats I would like to see to compare model performance when we perform experimentation:
Originally posted by @sushantkhare in https://github.com/pegasystems/pega-datascientist-tools/discussions/153#discussioncomment-7434953