public class RandomForestClassificationModel extends ProbabilisticClassificationModel<Vector,RandomForestClassificationModel> implements RandomForestClassifierParams, TreeEnsembleModel<DecisionTreeClassificationModel>, MLWritable, scala.Serializable, HasTrainingSummary<RandomForestClassificationTrainingSummary>
param: _trees Decision trees in the ensemble. Warning: These have null parents.
Modifier and Type | Method and Description |
---|---|
BinaryRandomForestClassificationTrainingSummary |
binarySummary()
Gets summary of model on training set.
|
BooleanParam |
bootstrap()
Whether bootstrap samples are used when building trees.
|
BooleanParam |
cacheNodeIds()
If false, the algorithm will pass trees to executors to match instances with nodes.
|
IntParam |
checkpointInterval()
Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
|
RandomForestClassificationModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
RandomForestClassificationSummary |
evaluate(Dataset<?> dataset)
Evaluates the model on a test dataset.
|
Vector |
featureImportances() |
Param<String> |
featureSubsetStrategy()
The number of features to consider for splits at each tree node.
|
Param<String> |
impurity()
Criterion used for information gain calculation (case-insensitive).
|
Param<String> |
leafCol()
Leaf indices column name.
|
static RandomForestClassificationModel |
load(String path) |
IntParam |
maxBins()
Maximum number of bins used for discretizing continuous features and for choosing how to split
on features at each node.
|
IntParam |
maxDepth()
Maximum depth of the tree (nonnegative).
|
IntParam |
maxMemoryInMB()
Maximum memory in MB allocated to histogram aggregation.
|
DoubleParam |
minInfoGain()
Minimum information gain for a split to be considered at a tree node.
|
IntParam |
minInstancesPerNode()
Minimum number of instances each child must have after split.
|
DoubleParam |
minWeightFractionPerNode()
Minimum fraction of the weighted sample count that each child must have after split.
|
int |
numClasses()
Number of classes (values which the label can take).
|
int |
numFeatures()
Returns the number of features the model was trained on.
|
IntParam |
numTrees()
Number of trees to train (at least 1).
|
Vector |
predictRaw(Vector features)
Raw prediction for each possible label.
|
static MLReader<RandomForestClassificationModel> |
read() |
LongParam |
seed()
Param for random seed.
|
DoubleParam |
subsamplingRate()
Fraction of the training data used for learning each decision tree, in range (0, 1].
|
RandomForestClassificationTrainingSummary |
summary()
Gets summary of model on training set.
|
String |
toString()
Summary of the model
|
int |
totalNumNodes()
Total number of nodes, summed over all trees in the ensemble.
|
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms dataset by reading from
featuresCol , and appending new columns as specified by
parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector . |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
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.
|
Param<String> |
weightCol()
Param for weight column name.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
normalizeToProbabilitiesInPlace, predictProbability, probabilityCol, setProbabilityCol, setThresholds, thresholds
predict, rawPredictionCol, setRawPredictionCol, transformImpl
featuresCol, labelCol, predictionCol, setFeaturesCol, setPredictionCol
transform, transform, transform
params
getBootstrap, getNumTrees
validateAndTransformSchema
getFeatureSubsetStrategy, getOldStrategy, getSubsamplingRate
getCacheNodeIds, getLeafCol, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getMinWeightFractionPerNode, getOldStrategy, setLeafCol
getCheckpointInterval
getWeightCol
getLabelCol, labelCol
featuresCol, getFeaturesCol
getPredictionCol, predictionCol
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
getRawPredictionCol, rawPredictionCol
getProbabilityCol, probabilityCol
getThresholds, thresholds
getImpurity, getOldImpurity
getLeafField, javaTreeWeights, predictLeaf, toDebugString
save
hasSummary, setSummary
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
public static MLReader<RandomForestClassificationModel> read()
public static RandomForestClassificationModel load(String path)
public int totalNumNodes()
TreeEnsembleModel
totalNumNodes
in interface TreeEnsembleModel<DecisionTreeClassificationModel>
public final Param<String> impurity()
TreeClassifierParams
impurity
in interface TreeClassifierParams
public final IntParam numTrees()
RandomForestParams
Note: The reason that we cannot add this to both GBT and RF (i.e. in TreeEnsembleParams)
is the param maxIter
controls how many trees a GBT has. The semantics in the algorithms
are a bit different.
numTrees
in interface RandomForestParams
public final BooleanParam bootstrap()
RandomForestParams
bootstrap
in interface RandomForestParams
public final DoubleParam subsamplingRate()
TreeEnsembleParams
subsamplingRate
in interface TreeEnsembleParams
public final Param<String> featureSubsetStrategy()
TreeEnsembleParams
These various settings are based on the following references: - log2: tested in Breiman (2001) - sqrt: recommended by Breiman manual for random forests - The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.
featureSubsetStrategy
in interface TreeEnsembleParams
public final Param<String> leafCol()
DecisionTreeParams
leafCol
in interface DecisionTreeParams
public final IntParam maxDepth()
DecisionTreeParams
maxDepth
in interface DecisionTreeParams
public final IntParam maxBins()
DecisionTreeParams
maxBins
in interface DecisionTreeParams
public final IntParam minInstancesPerNode()
DecisionTreeParams
minInstancesPerNode
in interface DecisionTreeParams
public final DoubleParam minWeightFractionPerNode()
DecisionTreeParams
minWeightFractionPerNode
in interface DecisionTreeParams
public final DoubleParam minInfoGain()
DecisionTreeParams
minInfoGain
in interface DecisionTreeParams
public final IntParam maxMemoryInMB()
DecisionTreeParams
maxMemoryInMB
in interface DecisionTreeParams
public final BooleanParam cacheNodeIds()
DecisionTreeParams
cacheNodeIds
in interface DecisionTreeParams
public final Param<String> weightCol()
HasWeightCol
weightCol
in interface HasWeightCol
public final LongParam seed()
HasSeed
public final IntParam checkpointInterval()
HasCheckpointInterval
checkpointInterval
in interface HasCheckpointInterval
public String uid()
Identifiable
uid
in interface Identifiable
public int numFeatures()
PredictionModel
numFeatures
in class PredictionModel<Vector,RandomForestClassificationModel>
public int numClasses()
ClassificationModel
numClasses
in class ClassificationModel<Vector,RandomForestClassificationModel>
public DecisionTreeClassificationModel[] trees()
TreeEnsembleModel
trees
in interface TreeEnsembleModel<DecisionTreeClassificationModel>
public double[] treeWeights()
TreeEnsembleModel
trees
treeWeights
in interface TreeEnsembleModel<DecisionTreeClassificationModel>
public RandomForestClassificationTrainingSummary summary()
hasSummary
is false.summary
in interface HasTrainingSummary<RandomForestClassificationTrainingSummary>
public BinaryRandomForestClassificationTrainingSummary binarySummary()
hasSummary
is false or it is a multiclass model.public RandomForestClassificationSummary evaluate(Dataset<?> dataset)
dataset
- Test dataset to evaluate model on.public StructType transformSchema(StructType schema)
PipelineStage
We check validity for interactions between parameters during transformSchema
and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate()
.
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema
in class ProbabilisticClassificationModel<Vector,RandomForestClassificationModel>
schema
- (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
ProbabilisticClassificationModel
featuresCol
, and appending new columns as specified by
parameters:
- predicted labels as predictionCol
of type Double
- raw predictions (confidences) as rawPredictionCol
of type Vector
- probability of each class as probabilityCol
of type Vector
.
transform
in class ProbabilisticClassificationModel<Vector,RandomForestClassificationModel>
dataset
- input datasetpublic Vector predictRaw(Vector features)
ClassificationModel
transform()
and output rawPredictionCol
.
predictRaw
in class ClassificationModel<Vector,RandomForestClassificationModel>
features
- (undocumented)public RandomForestClassificationModel copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Model<RandomForestClassificationModel>
extra
- (undocumented)public String toString()
TreeEnsembleModel
toString
in interface TreeEnsembleModel<DecisionTreeClassificationModel>
toString
in interface Identifiable
toString
in class Object
public Vector featureImportances()
public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable