I have built a binary classification model using:
- decision trees
- random forest
- bagging classifier
I have evaluated the above models and chose xgboost based on training/test and validation metrics (accuracy, prediction, recall, f1 and AUC).
I want to now productionalize it and share the output with the business. The output would basically have a list of items with the predicted class and that could be filtered based on business needs.
However, Instead of simply giving the business the predicted classes, I want to add insights/recommendations as to why a specific item was predicted with class X and how you could go about working on the item to change its class from say X to Y.
How do I go about this? I thought of using feature importance, but my input data shape is [800,000 * 1,050] and I am not sure if it would the best way to proceed.
Are there any existing industry standard methodologies that can add interpretability to such models and convert them from a black box models to prescriptive models?