public final class RandomForestRegressor extends Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>
Random Forest
learning algorithm for regression.
It supports both continuous and categorical features.Constructor and Description |
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RandomForestRegressor() |
RandomForestRegressor(String uid) |
copy, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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 RandomForestRegressor(String uid)
public RandomForestRegressor()
public static final String[] supportedImpurities()
public static final String[] supportedFeatureSubsetStrategies()
public String uid()
public RandomForestRegressor setMaxDepth(int value)
public RandomForestRegressor setMaxBins(int value)
public RandomForestRegressor setMinInstancesPerNode(int value)
public RandomForestRegressor setMinInfoGain(double value)
public RandomForestRegressor setMaxMemoryInMB(int value)
public RandomForestRegressor setCacheNodeIds(boolean value)
public RandomForestRegressor setCheckpointInterval(int value)
public RandomForestRegressor setImpurity(String value)
public RandomForestRegressor setSubsamplingRate(double value)
public RandomForestRegressor setSeed(long value)
public RandomForestRegressor setNumTrees(int value)
public RandomForestRegressor setFeatureSubsetStrategy(String value)
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.