public class RandomForestRegressionModel extends RegressionModel<Vector,RandomForestRegressionModel> implements RandomForestRegressorParams, TreeEnsembleModel<DecisionTreeRegressionModel>, MLWritable, scala.Serializable
param: _trees Decision trees in the ensemble. param: numFeatures Number of features used by this model
Modifier and Type | Method and Description |
---|---|
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).
|
RandomForestRegressionModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
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 RandomForestRegressionModel |
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 |
numFeatures()
Returns the number of features the model was trained on.
|
IntParam |
numTrees()
Number of trees to train (at least 1).
|
double |
predict(Vector features)
Predict label for the given features.
|
static MLReader<RandomForestRegressionModel> |
read() |
LongParam |
seed()
Param for random seed.
|
DoubleParam |
subsamplingRate()
Fraction of the training data used for learning each decision tree, in range (0, 1].
|
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 , calling predict , and storing
the predictions as a new column predictionCol . |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
DecisionTreeRegressionModel[] |
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. |
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
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
getCheckpointInterval
getWeightCol
getImpurity, getOldImpurity
getLeafField, javaTreeWeights, predictLeaf, toDebugString
save
$init$, 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<RandomForestRegressionModel> read()
public static RandomForestRegressionModel load(String path)
public int totalNumNodes()
TreeEnsembleModel
totalNumNodes
in interface TreeEnsembleModel<DecisionTreeRegressionModel>
public final Param<String> impurity()
HasVarianceImpurity
impurity
in interface HasVarianceImpurity
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,RandomForestRegressionModel>
public DecisionTreeRegressionModel[] trees()
TreeEnsembleModel
trees
in interface TreeEnsembleModel<DecisionTreeRegressionModel>
public double[] treeWeights()
TreeEnsembleModel
trees
treeWeights
in interface TreeEnsembleModel<DecisionTreeRegressionModel>
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 PredictionModel<Vector,RandomForestRegressionModel>
schema
- (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
PredictionModel
featuresCol
, calling predict
, and storing
the predictions as a new column predictionCol
.
transform
in class PredictionModel<Vector,RandomForestRegressionModel>
dataset
- input datasetpredictionCol
of type Double
public double predict(Vector features)
PredictionModel
transform()
and output predictionCol
.predict
in class PredictionModel<Vector,RandomForestRegressionModel>
features
- (undocumented)public RandomForestRegressionModel copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Model<RandomForestRegressionModel>
extra
- (undocumented)public String toString()
TreeEnsembleModel
toString
in interface TreeEnsembleModel<DecisionTreeRegressionModel>
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