I have a dataset with ~6M points, 9 features and two classes. The minority class represents just under 2% of the data. The data is first divided into 100 batches and a different classifier is trained for each batch using the rest of the data.
So far I have tried using sklearn's Naive Bayes classifier trained first using all the data, then downsampling, and then SMOTE. Every time I get this weird shape on the PR curve. I am now trying with random forest, but I am not confident. Does this suggest that the features design is bad? Any suggestions on what would be my next step?