A: Try Penalized Models
You can use the same algorithms but give them a different perspective on the problem.
Penalized classification imposes an additional cost on the model for making classification mistakes on the minority class during training. These penalties can bias the model to pay more attention to the minority class.
B: You can change the dataset that you use to build your predictive model to have more balanced data.
This change is called sampling your dataset and there are two main methods that you can use to even-up the classes:
* Consider testing under-sampling when you have an a lot data (tens- or hundreds of thousands of instances or more)
* Consider testing over-sampling when you don't have a lot of data (tens of thousands of records or less) D: Try Generate Synthetic Samples A simple way to generate synthetic samples is to randomly sample the attributes from instances in the minority class.
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/