Xplainable as a surrogate model

[tim-huntley]

Proposed Feature

Currently, xplainable has a suite of standalone solid models designed to be inherently explainable. We would also like to implement a new feature that allows xplainable to be used as a surrogate model to explain other complex models.

Vision

from xplainable.surrogates import XClfSurrogate

# Other code here ...

# Some complex model
model = XGBClassifier()
model.fit(X_train, y_train)

surrogate = XClfSurrogate()
explainer = surrogate.fit(model, X_train, y_train)

explainer.explain()