XGBoost Tree Ensemble Learner

Learns a tree based XGBoost model for classification. XGBoost is a popular machine learning library that is based on the ideas of boosting. Checkout the official documentation for some tutorials on how XGBoost works. Since XGBoost requires its features to be single precision floats, we automatically cast double precision values to float, which can cause problems for extreme numbers.

Input ports

1. Type: Table
   Input Data
   The data to learn from.

Output ports

1. Type: XGBoostModel
   XGBoost Model
   The trained model.
2. Type: Table
   XGBoost Feature Importance
   The feature importance measures for the training features. If the values are missing, then this indicates that the feature isn't used by the model at all.

   • Feature name column: The column containing feature names.
   • Weight column: The weight of a feature is the number of times a feature is used to split the data across all trees.
   • Gain column: The gain implies the average gain across all splits the feature is used in. A higher value of this metric when compared to another feature implies it is more important for generating a prediction.
   • Cover column: The cover of a feature is the average coverage across all splits the feature is used in.
   • Total gain column: The total gain sums up the gain across all splits the feature is used in.
   • Total cover column: The total cover sums up the total coverage across all splits the feature is used in.