org.apache.spark.ml.regression
Class RandomForestRegressor
Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<M>
org.apache.spark.ml.Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>
org.apache.spark.ml.regression.RandomForestRegressor
- All Implemented Interfaces:
- java.io.Serializable, Logging, Params
public final class RandomForestRegressor
- extends Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>
:: Experimental ::
Random Forest
learning algorithm for regression.
It supports both continuous and categorical features.
- See Also:
- Serialized Form
Methods inherited from class Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface org.apache.spark.ml.param.Params |
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, setDefault, shouldOwn, validateParams |
Methods inherited from interface org.apache.spark.Logging |
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning |
RandomForestRegressor
public RandomForestRegressor(String uid)
RandomForestRegressor
public RandomForestRegressor()
supportedImpurities
public static final String[] supportedImpurities()
- Accessor for supported impurity settings: variance
supportedFeatureSubsetStrategies
public static final String[] supportedFeatureSubsetStrategies()
- Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
uid
public String uid()
setMaxDepth
public RandomForestRegressor setMaxDepth(int value)
setMaxBins
public RandomForestRegressor setMaxBins(int value)
setMinInstancesPerNode
public RandomForestRegressor setMinInstancesPerNode(int value)
setMinInfoGain
public RandomForestRegressor setMinInfoGain(double value)
setMaxMemoryInMB
public RandomForestRegressor setMaxMemoryInMB(int value)
setCacheNodeIds
public RandomForestRegressor setCacheNodeIds(boolean value)
setCheckpointInterval
public RandomForestRegressor setCheckpointInterval(int value)
setImpurity
public RandomForestRegressor setImpurity(String value)
setSubsamplingRate
public RandomForestRegressor setSubsamplingRate(double value)
setSeed
public RandomForestRegressor setSeed(long value)
setNumTrees
public RandomForestRegressor setNumTrees(int value)
setFeatureSubsetStrategy
public RandomForestRegressor setFeatureSubsetStrategy(String value)
copy
public RandomForestRegressor copy(ParamMap extra)
- Description copied from interface:
Params
- Creates a copy of this instance with the same UID and some extra params.
Subclasses should implement this method and set the return type properly.
- Specified by:
copy
in interface Params
- Specified by:
copy
in class Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>
- Parameters:
extra
- (undocumented)
- Returns:
- (undocumented)
- See Also:
defaultCopy()
validateAndTransformSchema
public StructType validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
- Validates and transforms the input schema with the provided param map.
- Parameters:
schema
- input schemafitting
- whether this is in fittingfeaturesDataType
- SQL DataType for FeaturesType.
E.g., VectorUDT
for vector features.
- Returns:
- output schema