scikit-learn adding rules to classification model
As far as I know, there are two ways to solve this problem but it is not inside an SVM.
First Way - series
Implementing a rule-based classifier first then applying SVM...
Second way - Parallel
Implementing a rule-based classifier and SVM parallel and choosing the best one in the end layer combining together.
e.x Ensemble learning
both ways probably work in some cases, but you should try and see the results to choose the best way I guess the second one might work better.
I'm trying to create a model that predicts customer status change.
To give context, there are 4 statuses a customer can have: [A, B, C, D]
Each customer must have one status, and that status can change. I'm making a model with the current status as one of the features and the next status as the label.
Is there a way to hardcode a rule into SVM (or other classifiers) that prevents the model from classifying the label as the current status? In other words, if a customer's current status is A, its next status cannot be A, it has to be either B, C, or D.
If anyone knows whether sklearn has similar capabilities that would help.