public final class RandomForestRegressionModel extends PredictionModel<Vector,RandomForestRegressionModel> implements scala.Serializable
Random Forest
model for regression.
It supports both continuous and categorical features.
param: _trees Decision trees in the ensemble.Modifier and Type | Method and Description |
---|---|
RandomForestRegressionModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
static RandomForestRegressionModel |
fromOld(RandomForestModel oldModel,
RandomForestRegressor parent,
scala.collection.immutable.Map<Object,Object> categoricalFeatures)
(private[ml]) Convert a model from the old API
|
String |
toString() |
org.apache.spark.ml.tree.DecisionTreeModel[] |
trees() |
double[] |
treeWeights() |
String |
uid() |
StructType |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
Validates and transforms the input schema with the provided param map.
|
setFeaturesCol, setPredictionCol, transform, transformSchema
transform, transform, transform
clear, copyValues, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, setDefault, shouldOwn, validateParams
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public static RandomForestRegressionModel fromOld(RandomForestModel oldModel, RandomForestRegressor parent, scala.collection.immutable.Map<Object,Object> categoricalFeatures)
public String uid()
public org.apache.spark.ml.tree.DecisionTreeModel[] trees()
public double[] treeWeights()
public RandomForestRegressionModel copy(ParamMap extra)
Params
copy
in interface Params
copy
in class Model<RandomForestRegressionModel>
extra
- (undocumented)public String toString()
toString
in class Object
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
schema
- input schemafitting
- whether this is in fittingfeaturesDataType
- SQL DataType for FeaturesType.
E.g., VectorUDT
for vector features.