org.apache.spark.ml.classification
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" )
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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
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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
-
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()
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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 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
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
-
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
-
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.
-
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
- predicted labels as predictionCol of type
-
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 byParam.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 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.