Currently our machine learning model is not performing well for some revision cycles, i.e, the shorter the revision cycle the sensitivity needs to be high, the higher it is the sensitivity needs to be low. Here are few reasons why its happening:
We have different models for different revision cycle
The data is non-linear with 6 factors some going x direction other going y direction
We are using raw scoring
We are going to train a new deep learning model that would provide consistent recommendations regardless of revision cycle. The scores will finally depend on percentage instead of raw prediction.
Currently our machine learning model is not performing well for some revision cycles, i.e, the shorter the revision cycle the sensitivity needs to be high, the higher it is the sensitivity needs to be low. Here are few reasons why its happening:
We are going to train a new deep learning model that would provide consistent recommendations regardless of revision cycle. The scores will finally depend on percentage instead of raw prediction.