Xgboost Quantile Regression In R. How can we use a regression model to perform a binary classification. Aug 02 2016 I know that sklearnensembleGradientBoostingRegressor supports quantile regression and the production of prediction intervals.
Quantile Regression with XGBoost. Both the scikit-learn GradientBoostingRegressor and CatBoost implementations provide a way to compute these using Quantile Regression objective functions but both use the non-smooth standard definition of this regression. All else being equal we would prefer to more flexibly approximate with as opposed to eg.
I am using the python code shared on this blog and not really understanding how the quantile parameters affect the model I am using the suggested parameter values on the blogWhen I apply this code to my data I obtain nonsense results such as negative predictions.
Are there any plans for the XGBoost package to offer similar support. One way of doing this flexible approximation that work. XGBoost is the most popular boosting technique in recent times. If we think about the meaning of a regression applied to our data the numbers we get are probabilities that a datum will be classified as 1.
