Suppose I have successfully trained a XGBoost machine learning model in python.
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=7) model = XGBClassifier() model.fit(x_train, y_train) y_pred = model.predict(x_test)
I want to port this model to another system which will be written in C/C++. To do this, I need to know the internal logic of the XGboost trained model and translate them into a series of if-then-else statements like decision trees, if I am not wrong.
How can this be done? How to find out the internal logic of the XGBoost trained model to implement it on another system?
I am using python 3.7.