My dataset is of shape –
5621*8 (binary classification)
Label/target : Success (4324, 77 %) & Not success (1297, 23 %)
(success and Not success were been equally important for my prediction i.e, TP & TN)
I split my data into 3 (Train, Validate, test)
For train & Validate i perform 10 fold CV.
Test is the seperate data, which I evaluate for each folds
I tune my
scale_pos_weight ranging between
5 to 80, and
- Finally I fixed my values as 75 since I got average higher accuracy rate for my
Test set (79 %)for those 10 folds
- But, If i check my
average auc_roc metrics it is very poor, i.e
only 50 % for all 10 folds.
If i did not tune scale_pos_weight my
avg.accuracy drops to 50% & my avg auc_roc increases to 70 %.
How can I interpret from the above results between AUCROC & Accuracy in this situation?
What might be the problem in my case?