1.1 KiB
1.1 KiB
XGBoost Python Feature Walkthrough
- Basic walkthrough of wrappers
- Customize loss function, and evaluation metric
- Re-implement RMSLE as customized metric and objective
- Re-Implement
multi:softmaxobjective as customized objective - Boosting from existing prediction
- Predicting using first n trees
- Generalized Linear Model
- Cross validation
- Predicting leaf indices
- Sklearn Wrapper
- Sklearn Parallel
- Sklearn access evals result
- Access evals result
- External Memory
- Training continuation
- Feature weights for column sampling
- Basic Categorical data support
- Compare builtin categorical data support with one-hot encoding