I have a binary classification problem where I have around 15 features. I have chosen these features using some other model. Now I want to perform Bayesian Logistic on these features. My target classes are highly imbalance(minority class is 0.001%) and I have around 6 million records. I want to build a model which can be trained nighty or weekend using Bayesian logistic.

Currently, I have divided the data into 15 parts and then I train my model on the first part and test on the last part then I am updating my priors using `Interpolated`

method of `pymc3`

and rerun the model using the 2nd set of data. I am checking the accuracy and other metrics(ROC, f1-score) after each run.

Problems:

- My score is not improving.
- Am I using the right approch?
- This process is taking too much time.

If someone can guide me with the right approach and code snippets it will be very helpful for me.