public class RandomForestClassificationModel extends ProbabilisticClassificationModel<Vector,RandomForestClassificationModel> implements RandomForestClassifierParams, TreeEnsembleModel<DecisionTreeClassificationModel>, MLWritable, scala.Serializable
param: _trees Decision trees in the ensemble. Warning: These have null parents.
| Modifier and Type | Method and Description |
|---|---|
RandomForestClassificationModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Vector |
featureImportances()
Estimate of the importance of each feature.
|
static RandomForestClassificationModel |
load(String path) |
int |
numClasses()
Number of classes (values which the label can take).
|
int |
numFeatures()
Returns the number of features the model was trained on.
|
static MLReader<RandomForestClassificationModel> |
read() |
String |
toString()
Summary of the model
|
DecisionTreeClassificationModel[] |
trees()
Trees in this ensemble.
|
double[] |
treeWeights()
Weights for each tree, zippable with
trees |
String |
uid()
An immutable unique ID for the object and its derivatives.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
normalizeToProbabilitiesInPlace, setProbabilityCol, setThresholds, transformpredict, setRawPredictionColsetFeaturesCol, setPredictionCol, transformSchematransform, transform, transformgetNumTrees, numTrees, setNumTreesfeatureSubsetStrategy, getFeatureSubsetStrategy, getOldStrategy, getSubsamplingRate, setFeatureSubsetStrategy, setSubsamplingRate, subsamplingRatecacheNodeIds, getCacheNodeIds, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getOldStrategy, maxBins, maxDepth, maxMemoryInMB, minInfoGain, minInstancesPerNode, setCacheNodeIds, setCheckpointInterval, setMaxBins, setMaxDepth, setMaxMemoryInMB, setMinInfoGain, setMinInstancesPerNode, setSeedvalidateAndTransformSchemagetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwncheckpointInterval, getCheckpointIntervalgetImpurity, getOldImpurity, impurity, setImpurityjavaTreeWeights, toDebugString, totalNumNodessavevalidateAndTransformSchemagetRawPredictionCol, rawPredictionColgetProbabilityCol, probabilityColgetThresholds, thresholdsinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningpublic static MLReader<RandomForestClassificationModel> read()
public static RandomForestClassificationModel load(String path)
public String uid()
Identifiableuid in interface Identifiablepublic int numFeatures()
PredictionModelnumFeatures in class PredictionModel<Vector,RandomForestClassificationModel>public int numClasses()
ClassificationModelnumClasses in class ClassificationModel<Vector,RandomForestClassificationModel>public DecisionTreeClassificationModel[] trees()
TreeEnsembleModeltrees in interface TreeEnsembleModel<DecisionTreeClassificationModel>public double[] treeWeights()
TreeEnsembleModeltreestreeWeights in interface TreeEnsembleModel<DecisionTreeClassificationModel>public RandomForestClassificationModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<RandomForestClassificationModel>extra - (undocumented)public String toString()
TreeEnsembleModeltoString in interface TreeEnsembleModel<DecisionTreeClassificationModel>toString in interface IdentifiabletoString in class Objectpublic Vector featureImportances()
Each feature's importance is the average of its importance across all trees in the ensemble The importance vector is normalized to sum to 1. This method is suggested by Hastie et al. (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.) and follows the implementation from scikit-learn.
DecisionTreeClassificationModel.featureImportancespublic MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable