We have a legacy rule based model as a binary classier (0 vs 1). We want to replace it with a ML model. This rule based model uses only 4 features (age, race, gender, salary) and output 0/1. We have 20 extra features. So in total there are 24 features for 10 M unlabelled data points.
Here is our planned process:
- Use this rule based model to generate label for this 10 M data points (using only 4 features)
- Use ML models( like RF/logistic regression) to train on these data. The ML model will use 20+4 features to try to predict the label generated by rule based model
- Will this ML model has any chance to outperform the rule based model?
- Will it be able to "extrapolate" from label generated by rule based model? Or will it just use the extra 20 features to add noise?
- Any document / site discussing about this approach?