@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("DTRegressor")
@Description("A building stage for an Apache Spark ML Decision Tree regressor model. "
+ "This stage expects a dataset with at least two fields to train the model: One as "
+ "an array of numeric values, and, another that describes the class or label value "
+ "as numeric value.")
public class DTRegressor extends RegressorSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("DTPredictor")
@Description("A prediction stage that leverages a trained Apache Spark ML Decision Tree classifier "
+ "or regressor model. The model type parameter determines whether this stage predicts from a "
+ "classifier or regressor model.")
public class DTPredictor extends PredictorCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("GLRegressor")
@Description("A building stage for an Apache Spark ML Generalized Linear regressor model. This stage "
+ "expects a dataset with at least two fields to train the model: One as an array of numeric values, "
+ "and, another that describes the class or label value as numeric value.")
public class GLRegressor extends RegressorSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("GLPredictor")
@Description("A prediction stage that leverages a trained Apache Spark ML Generalized Linear "
+ "Regression (regressor) model.")
public class GLPredictor extends PredictorCompute {

      ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("GBTRegressor")
@Description("A building stage for an Apache Spark ML Gradient-Boosted Tree regressor model. "
+ "This stage expects a dataset with at least two fields to train the model: One as an array "
+ "of numeric values, and, another that describes the class or label value as numeric value.")
public class GBTRegressor extends RegressorSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("GBTPredictor")
@Description("A prediction stage that leverages a trained Apache Spark based Gradient-Boosted Trees "
+ "classifier or regressor model. The model type parameter determines whether this stage predicts "
+ "from a classifier or regressor model.")

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("IsotonicRegressor")
@Description("A building stage for an Apache Spark ML Isotonic Regression (regressor) model. "
+ "This stage expects a dataset with at least two fields to train the model: One as an array "
+ "of numeric values, and, another that describes the class or label value as numeric value.")
public class IsotonicRegressor extends RegressorSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("IsotonicPredictor")
@Description("A prediction stage that leverages a trained Apache Spark ML Isotonic Regression "
+ "(regressor) model.")
public class IsotonicPredictor extends PredictorCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("LinearRegressor")
@Description("A building stage for an Apache Spark ML Linear Regression (regressor) model. "
+ "This stage expects a dataset with at least two fields to train the model: One as an "
+ "array of numeric values, and, another that describes the class or label value as "
+ "numeric value.")
public class LinearRegressor extends RegressorSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("LinearPredictor")
@Description("A prediction stage that leverages a trained Apache Spark ML Linear Regression "
+ "(regressor) model.")
public class LinearPredictor extends PredictorCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("RFRegressor")
@Description("A building stage for an Apache Spark ML Random Forest Trees regressor model. "
+ "This stage expects a dataset with at least two fields to train the model: One as an "
+ "array of numeric values, and, another that describes the class or label value as numeric value.")
public class RFRegressor extends RegressorSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("RFPredictor")
@Description("A prediction stage that leverages a trained Apache Spark ML Random Forest "
+ "classifier or regressor model. The model type parameter determines whether this stage "
+ "predicts from a classifier or regressor model.")		
public class RFPredictor extends PredictorCompute {

    ...

}

@Plugin(type = SparkSink.PLUGIN_TYPE)
@Name("SurvivalRegressor")
@Description("A building stage for an Apache Spark ML Survival (AFT) regressor model. "
+ "This stage expects a dataset with at least two fields to train the model: One as "
+ "an array of numeric values, and, another that describes the class or label value as "
+ "numeric value.")
public class SurvivalRegressor extends RegressorSink {

    ...

}

@Plugin(type = SparkCompute.PLUGIN_TYPE)
@Name("SurvivalPredictor")
@Description("A prediction stage that leverages a trained Apache Spark ML Survival (AFT) "
+ "regressor model.")
public class SurvivalPredictor extends PredictorCompute {

      ...

}