Convert an instance of xgboost.Booster into a model that implements the scikit-learn API

Question

I am trying to use mlflow to save a model and then load it later to make predictions.

I'm using a xgboost.XGBRegressor model and its sklearn functions .predict() and .predict_proba() to make predictions but it turns out that mlflow doesn't support models that implements the sklearn API, so when loading the model later from mlflow, mlflow returns an instance of xgboost.Booster, and it doesn't implements the .predict() or .predict_proba() functions.

Is there a way to convert a xgboost.Booster back into a xgboost.sklearn.XGBRegressor object that implements the sklearn API functions?

Answer 1

I have a xgboost.core.Booster object and it can make return probability calculations as follows your_Booster_model_object.predict(your_xgboost_dmatrix_dataset).