I was trying to solve the ‘German Credit Risk classification’ which aims at predicting if a customer has a good credit or a bad credit
The dataset has only 1000 rows and around 9 variable.
I tried almost all models and though CART and RF work better than other models , I am unable to push F1 score beyond 0.437 …
Tried adding feature(credit.amount/duration),add loss param in CART, log transformations and while all this does show improvement ,its not really impressive.
Will this score be too less for such a small data set?
Target Variable is credit.rating
Predictors : Age,
Job(0-unskilled,non resident,1 unskilled resident,2 -skilled resident,3-highly skilled)
Can someone please provide guidance