xgboost/demo/guide-python

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:softmax objective 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