This doc focuses on GPU related Python API interfaces. Four new classes are introduced to support ML things on spark.
The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier. It is a wrapper around Scala XGBoostClassifier.
- XGBoostClassifier(**params)
- all standard xgboost parameters are supported, but please note a few differences:
- only camelCase is supported when specifying parameter names, e.g., maxDepth
- parameter lambda is renamed to lambda_, because lambda is a keyword in Python
- all standard xgboost parameters are supported, but please note a few differences:
Note: Only GPU related methods are listed below.
- setFeaturesCols(features_cols). This method sets the feature columns for training.
- features_cols: a list of feature column names in String format to set
- returns the classifier itself
The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel. It is a wrapper around Scala XGBoostClassificationModel.
No GPU specific methods, use it as a normal spark model.
The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostRegressor. It is a wrapper around Scala XGBoostRegressor.
- XGBoostRegressor(**params)
- all standard xgboost parameters are supported, but please note a few differences:
- only camelCase is supported when specifying parameter names, e.g., maxDepth
- parameter lambda is renamed to lambda_, because lambda is a keyword in Python
- all standard xgboost parameters are supported, but please note a few differences:
Note: Only GPU related methods are listed below.
- setFeaturesCols(features_cols). This method sets the feature columns for training.
- features_cols: a list of feature column names in String format to set
- returns the regressor itself
The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostRegressionModel. It is a wrapper around Scala XGBoostRegressionModel.
No GPU specific methods, use it as a normal spark model.