org.apache.spark.ml.regression
RandomForestRegressionModel
Companion object RandomForestRegressionModel
class RandomForestRegressionModel extends PredictionModel[Vector, RandomForestRegressionModel] with RandomForestRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable
Random Forest model for regression. It supports both continuous and categorical features.
- Annotations
- @Since( "1.4.0" )
- Grouped
- Alphabetic
- By Inheritance
- RandomForestRegressionModel
- MLWritable
- TreeEnsembleModel
- RandomForestRegressorParams
- TreeRegressorParams
- HasVarianceImpurity
- RandomForestParams
- TreeEnsembleParams
- DecisionTreeParams
- HasSeed
- HasCheckpointInterval
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
$[T](param: Param[T]): T
An alias for
getOrDefault()
.An alias for
getOrDefault()
.- Attributes
- protected
- Definition Classes
- Params
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
final
val
cacheNodeIds: BooleanParam
If false, the algorithm will pass trees to executors to match instances with nodes.
If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default = false)
- Definition Classes
- DecisionTreeParams
-
final
val
checkpointInterval: IntParam
Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.
- Definition Classes
- HasCheckpointInterval
-
final
def
clear(param: Param[_]): RandomForestRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
def
copy(extra: ParamMap): RandomForestRegressionModel
Creates a copy of this instance with the same UID and some extra 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. See
defaultCopy()
.- Definition Classes
- RandomForestRegressionModel → Model → Transformer → PipelineStage → Params
- Annotations
- @Since( "1.4.0" )
-
def
copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to
defaultParamMap
, and explicitly set Params are copied from and toparamMap
. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.- to
the target instance, which should work with the same set of default Params as this source instance
- extra
extra params to be copied to the target's
paramMap
- returns
the target instance with param values copied
- Attributes
- protected
- Definition Classes
- Params
-
final
def
defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
- Attributes
- protected
- Definition Classes
- Params
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
-
def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam()
.- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
- Params
-
lazy val
featureImportances: Vector
Estimate of the importance of each feature.
Estimate of the importance of each feature.
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.
- Annotations
- @Since( "1.5.0" )
- See also
DecisionTreeRegressionModel.featureImportances
-
final
val
featureSubsetStrategy: Param[String]
The number of features to consider for splits at each tree node.
The number of features to consider for splits at each tree node. Supported options:
- "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
- "all": use all features
- "onethird": use 1/3 of the features
- "sqrt": use sqrt(number of features)
- "log2": use log2(number of features)
- "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")
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.
- Definition Classes
- TreeEnsembleParams
- See also
-
final
val
featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
-
def
featuresDataType: DataType
Returns the SQL DataType corresponding to the FeaturesType type parameter.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by
validateAndTransformSchema()
. This workaround is needed since SQL has different APIs for Scala and Java.The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.
- Attributes
- protected
- Definition Classes
- PredictionModel
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
-
final
def
getCacheNodeIds: Boolean
- Definition Classes
- DecisionTreeParams
-
final
def
getCheckpointInterval: Int
- Definition Classes
- HasCheckpointInterval
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
def
getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
-
final
def
getFeatureSubsetStrategy: String
- Definition Classes
- TreeEnsembleParams
-
final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
-
final
def
getImpurity: String
- Definition Classes
- HasVarianceImpurity
-
final
def
getLabelCol: String
- Definition Classes
- HasLabelCol
-
final
def
getMaxBins: Int
- Definition Classes
- DecisionTreeParams
-
final
def
getMaxDepth: Int
- Definition Classes
- DecisionTreeParams
-
final
def
getMaxMemoryInMB: Int
- Definition Classes
- DecisionTreeParams
-
final
def
getMinInfoGain: Double
- Definition Classes
- DecisionTreeParams
-
final
def
getMinInstancesPerNode: Int
- Definition Classes
- DecisionTreeParams
-
final
def
getNumTrees: Int
- Definition Classes
- RandomForestParams
-
final
def
getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
-
final
def
getSeed: Long
- Definition Classes
- HasSeed
-
final
def
getSubsamplingRate: Double
- Definition Classes
- TreeEnsembleParams
-
final
def
hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
-
def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
val
impurity: Param[String]
Criterion used for information gain calculation (case-insensitive).
Criterion used for information gain calculation (case-insensitive). Supported: "variance". (default = variance)
- Definition Classes
- HasVarianceImpurity
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
final
def
isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
final
val
labelCol: Param[String]
Param for label column name.
Param for label column name.
- Definition Classes
- HasLabelCol
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
final
val
maxBins: IntParam
Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.
Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be >= 2 and >= number of categories in any categorical feature. (default = 32)
- Definition Classes
- DecisionTreeParams
-
final
val
maxDepth: IntParam
Maximum depth of the tree (>= 0).
Maximum depth of the tree (>= 0). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)
- Definition Classes
- DecisionTreeParams
-
final
val
maxMemoryInMB: IntParam
Maximum memory in MB allocated to histogram aggregation.
Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default = 256 MB)
- Definition Classes
- DecisionTreeParams
-
final
val
minInfoGain: DoubleParam
Minimum information gain for a split to be considered at a tree node.
Minimum information gain for a split to be considered at a tree node. Should be >= 0.0. (default = 0.0)
- Definition Classes
- DecisionTreeParams
-
final
val
minInstancesPerNode: IntParam
Minimum number of instances each child must have after split.
Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1. (default = 1)
- Definition Classes
- DecisionTreeParams
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
val
numFeatures: Int
Returns the number of features the model was trained on.
Returns the number of features the model was trained on. If unknown, returns -1
- Definition Classes
- RandomForestRegressionModel → PredictionModel
-
final
val
numTrees: IntParam
Number of trees to train (>= 1).
Number of trees to train (>= 1). If 1, then no bootstrapping is used. If > 1, then bootstrapping is done. TODO: Change to always do bootstrapping (simpler). SPARK-7130 (default = 20)
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.- Definition Classes
- RandomForestParams
-
lazy val
params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
-
var
parent: Estimator[RandomForestRegressionModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
-
def
predict(features: Vector): Double
Predict label for the given features.
Predict label for the given features. This method is used to implement
transform()
and output predictionCol.- Definition Classes
- RandomForestRegressionModel → PredictionModel
-
final
val
predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
-
def
save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path)
.Saves this ML instance to the input path, a shortcut of
write.save(path)
.- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
val
seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
-
final
def
set(paramPair: ParamPair[_]): RandomForestRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): RandomForestRegressionModel.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): RandomForestRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
final
def
setDefault(paramPairs: ParamPair[_]*): RandomForestRegressionModel.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter
setDefault
. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.- paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): RandomForestRegressionModel.this.type
Sets a default value for a param.
Sets a default value for a param.
- param
param to set the default value. Make sure that this param is initialized before this method gets called.
- value
the default value
- Attributes
- protected
- Definition Classes
- Params
-
def
setFeaturesCol(value: String): RandomForestRegressionModel
- Definition Classes
- PredictionModel
-
def
setParent(parent: Estimator[RandomForestRegressionModel]): RandomForestRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
setPredictionCol(value: String): RandomForestRegressionModel
- Definition Classes
- PredictionModel
-
final
val
subsamplingRate: DoubleParam
Fraction of the training data used for learning each decision tree, in range (0, 1].
Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)
- Definition Classes
- TreeEnsembleParams
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toDebugString: String
Full description of model
Full description of model
- Definition Classes
- TreeEnsembleModel
-
def
toString(): String
Summary of the model
Summary of the model
- Definition Classes
- RandomForestRegressionModel → TreeEnsembleModel → Identifiable → AnyRef → Any
- Annotations
- @Since( "1.4.0" )
-
lazy val
totalNumNodes: Int
Total number of nodes, summed over all trees in the ensemble.
Total number of nodes, summed over all trees in the ensemble.
- Definition Classes
- TreeEnsembleModel
-
def
transform(dataset: Dataset[_]): DataFrame
Transforms dataset by reading from featuresCol, calling
predict
, and storing the predictions as a new column predictionCol.Transforms dataset by reading from featuresCol, calling
predict
, and storing the predictions as a new column predictionCol.- dataset
input dataset
- returns
transformed dataset with predictionCol of type
Double
- Definition Classes
- PredictionModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
def
transformImpl(dataset: Dataset[_]): DataFrame
- Attributes
- protected
- Definition Classes
- RandomForestRegressionModel → PredictionModel
-
def
transformSchema(schema: StructType): StructType
:: DeveloperApi ::
:: DeveloperApi ::
Check transform validity and derive the output schema from the input schema.
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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- PredictionModel → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
def
treeWeights: Array[Double]
Weights for each tree, zippable with trees
Weights for each tree, zippable with trees
- Definition Classes
- RandomForestRegressionModel → TreeEnsembleModel
- Annotations
- @Since( "1.4.0" )
-
def
trees: Array[DecisionTreeRegressionModel]
Trees in this ensemble.
Trees in this ensemble. Warning: These have null parent Estimators.
- Definition Classes
- RandomForestRegressionModel → TreeEnsembleModel
- Annotations
- @Since( "1.4.0" )
-
val
uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- RandomForestRegressionModel → Identifiable
-
def
validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
- schema
input schema
- fitting
whether this is in fitting
- featuresDataType
SQL DataType for FeaturesType. E.g.,
VectorUDT
for vector features.- returns
output schema
- Attributes
- protected
- Definition Classes
- PredictorParams
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
def
write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- RandomForestRegressionModel → MLWritable
- Annotations
- @Since( "2.0.0" )
Inherited from MLWritable
Inherited from TreeEnsembleModel[DecisionTreeRegressionModel]
Inherited from RandomForestRegressorParams
Inherited from TreeRegressorParams
Inherited from HasVarianceImpurity
Inherited from RandomForestParams
Inherited from TreeEnsembleParams
Inherited from DecisionTreeParams
Inherited from HasSeed
Inherited from HasCheckpointInterval
Inherited from PredictionModel[Vector, RandomForestRegressionModel]
Inherited from PredictorParams
Inherited from HasPredictionCol
Inherited from HasFeaturesCol
Inherited from HasLabelCol
Inherited from Model[RandomForestRegressionModel]
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
Members
Parameter setters
Parameter getters
(expert-only) Parameters
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.