RandomForestClassificationModel

Companion object RandomForestClassificationModel

class RandomForestClassificationModel extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel] with RandomForestClassifierParams with TreeEnsembleModel[DecisionTreeClassificationModel] with MLWritable with Serializable with HasTrainingSummary[RandomForestClassificationTrainingSummary]

Random Forest model for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

Annotations: @Since( "1.4.0" )
Source: RandomForestClassifier.scala

Linear Supertypes

HasTrainingSummary[RandomForestClassificationTrainingSummary], MLWritable, TreeEnsembleModel[DecisionTreeClassificationModel], RandomForestClassifierParams, TreeClassifierParams, TreeEnsembleClassifierParams, RandomForestParams, TreeEnsembleParams, DecisionTreeParams, HasWeightCol, HasSeed, HasCheckpointInterval, ProbabilisticClassificationModel[Vector, RandomForestClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, RandomForestClassificationModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, RandomForestClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[RandomForestClassificationModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

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RandomForestClassificationModel
HasTrainingSummary
MLWritable
TreeEnsembleModel
RandomForestClassifierParams
TreeClassifierParams
TreeEnsembleClassifierParams
RandomForestParams
TreeEnsembleParams
DecisionTreeParams
HasWeightCol
HasSeed
HasCheckpointInterval
ProbabilisticClassificationModel
ProbabilisticClassifierParams
HasThresholds
HasProbabilityCol
ClassificationModel
ClassifierParams
HasRawPredictionCol
PredictionModel
PredictorParams
HasPredictionCol
HasFeaturesCol
HasLabelCol
Model
Transformer
PipelineStage
Logging
Params
Serializable
Serializable
Identifiable
AnyRef
Any

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Value Members

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
def binarySummary: BinaryRandomForestClassificationTrainingSummary
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown if hasSummary is false or it is a multiclass model.

Annotations
@Since( "3.1.0" )
final val bootstrap: BooleanParam
Whether bootstrap samples are used when building trees.
Whether bootstrap samples are used when building trees.

Definition Classes
RandomForestParams
Annotations
@Since( "3.0.0" )
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[_]): RandomForestClassificationModel.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[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def copy(extra: ParamMap): RandomForestClassificationModel
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
RandomForestClassificationModel → 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 to paramMap. 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 evaluate(dataset: Dataset[_]): RandomForestClassificationSummary
Evaluates the model on a test dataset.
Evaluates the model on a test dataset.
dataset
Test dataset to evaluate model on.

Annotations
@Since( "3.1.0" )
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
def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

Attributes
protected
Definition Classes
ClassifierParams
def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

Attributes
protected
Definition Classes
PredictorParams
def extractInstances(dataset: Dataset[_]): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
PredictorParams
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
DecisionTreeClassificationModel.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 greater than 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
Breiman (2001)
Breiman manual for random forests
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[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 getBootstrap: Boolean

Definition Classes
RandomForestParams
Annotations
@Since( "3.0.0" )
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
TreeClassifierParams
final def getLabelCol: String

Definition Classes
HasLabelCol
final def getLeafCol: String

Definition Classes
DecisionTreeParams
Annotations
@Since( "3.0.0" )
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 getMinWeightFractionPerNode: Double

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 getProbabilityCol: String

Definition Classes
HasProbabilityCol
final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
final def getSeed: Long

Definition Classes
HasSeed
final def getSubsamplingRate: Double

Definition Classes
TreeEnsembleParams
def getThresholds: Array[Double]

Definition Classes
HasThresholds
final def getWeightCol: String

Definition Classes
HasWeightCol
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.
Indicates whether this Model has a corresponding parent.

Definition Classes
Model
def hasSummary: Boolean
Indicates whether a training summary exists for this model instance.
Indicates whether a training summary exists for this model instance.

Definition Classes
HasTrainingSummary
Annotations
@Since( "3.0.0" )
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). This impurity type is used in DecisionTreeClassifier and RandomForestClassifier, Supported: "entropy" and "gini". (default = gini)

Definition Classes
TreeClassifierParams
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): 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
final val leafCol: Param[String]
Leaf indices column name.
Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")

Definition Classes
DecisionTreeParams
Annotations
@Since( "3.0.0" )
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 at least 2 and at least number of categories in any categorical feature. (default = 32)

Definition Classes
DecisionTreeParams
final val maxDepth: IntParam
Maximum depth of the tree (nonnegative).
Maximum depth of the tree (nonnegative). 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 at least 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. Must be at least 1. (default = 1)

Definition Classes
DecisionTreeParams
final val minWeightFractionPerNode: DoubleParam
Minimum fraction of the weighted sample count that each child must have after split.
Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in the interval [0.0, 0.5). (default = 0.0)

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 numClasses: Int
Number of classes (values which the label can take).
Number of classes (values which the label can take).

Definition Classes
RandomForestClassificationModel → ClassificationModel
Annotations
@Since( "1.5.0" )
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
RandomForestClassificationModel → PredictionModel
Annotations
@Since( "1.6.0" )
final val numTrees: IntParam
Number of trees to train (at least 1).
Number of trees to train (at least 1). If 1, then no bootstrapping is used. If greater than 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[RandomForestClassificationModel]
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.
This default implementation for classification predicts the index of the maximum value from predictRaw().

Definition Classes
ClassificationModel → PredictionModel
def predictLeaf(features: Vector): Vector
returns
The indices of the leaves corresponding to the feature vector. Leaves are indexed in pre-order from 0.

Definition Classes
TreeEnsembleModel
def predictProbability(features: Vector): Vector
Predict the probability of each class given the features.
Predict the probability of each class given the features. These predictions are also called class conditional probabilities.
This internal method is used to implement transform() and output probabilityCol.
returns
Estimated class conditional probabilities

Definition Classes
ProbabilisticClassificationModel
Annotations
@Since( "3.0.0" )
def predictRaw(features: Vector): Vector
Raw prediction for each possible label.
Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.
returns
vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

Definition Classes
RandomForestClassificationModel → ClassificationModel
Annotations
@Since( "3.0.0" )
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol
def probability2prediction(probability: Vector): Double
Given a vector of class conditional probabilities, select the predicted label.
Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.
returns
predicted label

Attributes
protected
Definition Classes
ProbabilisticClassificationModel
final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Definition Classes
HasProbabilityCol
def raw2prediction(rawPrediction: Vector): Double
Given a vector of raw predictions, select the predicted label.
Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.
returns
predicted label

Attributes
protected
Definition Classes
ProbabilisticClassificationModel → ClassificationModel
def raw2probability(rawPrediction: Vector): Vector
Non-in-place version of raw2probabilityInPlace()
Non-in-place version of raw2probabilityInPlace()

Attributes
protected
Definition Classes
ProbabilisticClassificationModel
def raw2probabilityInPlace(rawPrediction: Vector): Vector
Estimate the probability of each class given the raw prediction, doing the computation in-place.
Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.
This internal method is used to implement transform() and output probabilityCol.
returns
Estimated class conditional probabilities (modified input vector)

Attributes
protected
Definition Classes
RandomForestClassificationModel → ProbabilisticClassificationModel
final val rawPredictionCol: Param[String]
Param for raw prediction (a.k.a.
Param for raw prediction (a.k.a. confidence) column name.

Definition Classes
HasRawPredictionCol
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[_]): RandomForestClassificationModel.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): RandomForestClassificationModel.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): RandomForestClassificationModel.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[_]*): RandomForestClassificationModel.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): RandomForestClassificationModel.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): RandomForestClassificationModel

Definition Classes
PredictionModel
final def setLeafCol(value: String): RandomForestClassificationModel.this.type

Definition Classes
DecisionTreeParams
Annotations
@Since( "3.0.0" )
def setParent(parent: Estimator[RandomForestClassificationModel]): RandomForestClassificationModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).

Definition Classes
Model
def setPredictionCol(value: String): RandomForestClassificationModel

Definition Classes
PredictionModel
def setProbabilityCol(value: String): RandomForestClassificationModel

Definition Classes
ProbabilisticClassificationModel
def setRawPredictionCol(value: String): RandomForestClassificationModel

Definition Classes
ClassificationModel
def setThresholds(value: Array[Double]): RandomForestClassificationModel

Definition Classes
ProbabilisticClassificationModel
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
def summary: RandomForestClassificationTrainingSummary
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown if hasSummary is false.

Definition Classes
RandomForestClassificationModel → HasTrainingSummary
Annotations
@Since( "3.1.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val thresholds: DoubleArrayParam
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

Definition Classes
HasThresholds
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
RandomForestClassificationModel → 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, and appending new columns as specified by parameters:
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.
dataset
input dataset
returns
transformed dataset

Definition Classes
RandomForestClassificationModel → ProbabilisticClassificationModel → ClassificationModel → 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()
final def transformImpl(dataset: Dataset[_]): DataFrame

Definition Classes
ClassificationModel → PredictionModel
def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
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 by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes
RandomForestClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
Annotations
@Since( "1.4.0" )
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
RandomForestClassificationModel → TreeEnsembleModel
Annotations
@Since( "1.4.0" )
def trees: Array[DecisionTreeClassificationModel]
Trees in this ensemble.
Trees in this ensemble. Warning: These have null parent Estimators.

Definition Classes
RandomForestClassificationModel → 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
RandomForestClassificationModel → Identifiable
Annotations
@Since( "1.5.0" )
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
TreeEnsembleClassifierParams → ProbabilisticClassifierParams → ClassifierParams → 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
@throws( ... ) @native()
final val weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Definition Classes
HasWeightCol
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
RandomForestClassificationModel → MLWritable
Annotations
@Since( "2.0.0" )

Inherited from HasTrainingSummary[RandomForestClassificationTrainingSummary]

Inherited from MLWritable

Inherited from TreeEnsembleModel[DecisionTreeClassificationModel]

Inherited from RandomForestClassifierParams

Inherited from TreeClassifierParams

Inherited from TreeEnsembleClassifierParams

Inherited from RandomForestParams

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasWeightCol

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from ProbabilisticClassificationModel[Vector, RandomForestClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, RandomForestClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, RandomForestClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[RandomForestClassificationModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

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.

Packages

RandomForestClassificationModel 

Companion object RandomForestClassificationModel

Value Members

Inherited from HasTrainingSummary[RandomForestClassificationTrainingSummary]

Inherited from MLWritable

Inherited from TreeEnsembleModel[DecisionTreeClassificationModel]

Inherited from RandomForestClassifierParams

Inherited from TreeClassifierParams

Inherited from TreeEnsembleClassifierParams

Inherited from RandomForestParams

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasWeightCol

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from ProbabilisticClassificationModel[Vector, RandomForestClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, RandomForestClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, RandomForestClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[RandomForestClassificationModel]

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

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter getters

RandomForestClassificationModel