org.apache.spark.ml.classification
Class RandomForestClassifier
Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<M>
org.apache.spark.ml.Predictor<Vector,RandomForestClassifier,RandomForestClassificationModel>
org.apache.spark.ml.classification.RandomForestClassifier
- All Implemented Interfaces:
- java.io.Serializable, Logging, Params
public final class RandomForestClassifier
- extends Predictor<Vector,RandomForestClassifier,RandomForestClassificationModel>
:: Experimental ::
Random Forest
learning algorithm for
classification.
It supports both binary and multiclass labels, as well as 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 |
RandomForestClassifier
public RandomForestClassifier(String uid)
RandomForestClassifier
public RandomForestClassifier()
supportedImpurities
public static final String[] supportedImpurities()
- Accessor for supported impurity settings: entropy, gini
supportedFeatureSubsetStrategies
public static final String[] supportedFeatureSubsetStrategies()
- Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
uid
public String uid()
setMaxDepth
public RandomForestClassifier setMaxDepth(int value)
setMaxBins
public RandomForestClassifier setMaxBins(int value)
setMinInstancesPerNode
public RandomForestClassifier setMinInstancesPerNode(int value)
setMinInfoGain
public RandomForestClassifier setMinInfoGain(double value)
setMaxMemoryInMB
public RandomForestClassifier setMaxMemoryInMB(int value)
setCacheNodeIds
public RandomForestClassifier setCacheNodeIds(boolean value)
setCheckpointInterval
public RandomForestClassifier setCheckpointInterval(int value)
setImpurity
public RandomForestClassifier setImpurity(String value)
setSubsamplingRate
public RandomForestClassifier setSubsamplingRate(double value)
setSeed
public RandomForestClassifier setSeed(long value)
setNumTrees
public RandomForestClassifier setNumTrees(int value)
setFeatureSubsetStrategy
public RandomForestClassifier setFeatureSubsetStrategy(String value)
copy
public RandomForestClassifier 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,RandomForestClassifier,RandomForestClassificationModel>
- 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