class GBTRegressionModel extends RegressionModel[Vector, GBTRegressionModel] with GBTRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable
Gradient-Boosted Trees (GBTs) model for regression. It supports both continuous and categorical features.
- Annotations
- @Since( "1.4.0" )
- Source
- GBTRegressor.scala
- Grouped
- Alphabetic
- By Inheritance
- GBTRegressionModel
- MLWritable
- TreeEnsembleModel
- GBTRegressorParams
- TreeRegressorParams
- HasVarianceImpurity
- TreeEnsembleRegressorParams
- GBTParams
- HasValidationIndicatorCol
- HasStepSize
- HasMaxIter
- TreeEnsembleParams
- DecisionTreeParams
- HasWeightCol
- HasSeed
- HasCheckpointInterval
- RegressionModel
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
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
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
-
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 DecisionTreeRegressor, RandomForestRegressor, GBTRegressor and GBTClassifier (since GBTClassificationModel is internally composed of DecisionTreeRegressionModels). Supported: "variance". (default = variance)
- Definition Classes
- HasVarianceImpurity
-
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" )
-
val
lossType: Param[String]
Loss function which GBT tries to minimize.
Loss function which GBT tries to minimize. (case-insensitive) Supported: "squared" (L2) and "absolute" (L1) (default = squared)
- Definition Classes
- GBTRegressorParams
-
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
maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
-
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
val
predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
-
final
val
seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
-
final
val
stepSize: DoubleParam
Param for Step size (a.k.a.
Param for Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default = 0.1)
- Definition Classes
- GBTParams → HasStepSize
-
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
val
validationIndicatorCol: Param[String]
Param for name of the column that indicates whether each row is for training or for validation.
Param for name of the column that indicates whether each row is for training or for validation. False indicates training; true indicates validation..
- Definition Classes
- HasValidationIndicatorCol
-
final
val
validationTol: DoubleParam
Threshold for stopping early when fit with validation is used.
Threshold for stopping early when fit with validation is used. (This parameter is ignored when fit without validation is used.) The decision to stop early is decided based on this logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01.
- Definition Classes
- GBTParams
- Annotations
- @Since( "2.4.0" )
- See also
validationIndicatorCol
-
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
Members
-
final
def
clear(param: Param[_]): GBTRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
copy(extra: ParamMap): GBTRegressionModel
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
- GBTRegressionModel → Model → Transformer → PipelineStage → Params
- Annotations
- @Since( "1.4.0" )
-
def
evaluateEachIteration(dataset: Dataset[_], loss: String): Array[Double]
Method to compute error or loss for every iteration of gradient boosting.
Method to compute error or loss for every iteration of gradient boosting.
- dataset
Dataset for validation.
- loss
The loss function used to compute error. Supported options: squared, absolute
- Annotations
- @Since( "2.4.0" )
-
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
extractParamMapwith no extra values.extractParamMapwith 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( "2.0.0" )
- See also
DecisionTreeRegressionModel.featureImportances
-
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
getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
-
val
getNumTrees: Int
Number of trees in ensemble
Number of trees in ensemble
- Annotations
- @Since( "2.0.0" )
-
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
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.
-
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
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
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
- GBTRegressionModel → PredictionModel
-
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[GBTRegressionModel]
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
- GBTRegressionModel → 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
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
def
set[T](param: Param[T], value: T): GBTRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setParent(parent: Estimator[GBTRegressionModel]): GBTRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
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
- GBTRegressionModel → 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
- GBTRegressionModel → 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
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
transformSchemaand 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
- GBTRegressionModel → PredictionModel → PipelineStage
- Annotations
- @Since( "1.4.0" )
-
def
treeWeights: Array[Double]
Weights for each tree, zippable with trees
Weights for each tree, zippable with trees
- Definition Classes
- GBTRegressionModel → 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
- GBTRegressionModel → 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
- GBTRegressionModel → Identifiable
-
def
write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- GBTRegressionModel → MLWritable
- Annotations
- @Since( "2.0.0" )
Parameter setters
-
def
setFeaturesCol(value: String): GBTRegressionModel
- Definition Classes
- PredictionModel
-
final
def
setLeafCol(value: String): GBTRegressionModel.this.type
- Definition Classes
- DecisionTreeParams
- Annotations
- @Since( "3.0.0" )
-
def
setPredictionCol(value: String): GBTRegressionModel
- Definition Classes
- PredictionModel
Parameter getters
-
final
def
getCheckpointInterval: Int
- Definition Classes
- HasCheckpointInterval
-
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
getLeafCol: String
- Definition Classes
- DecisionTreeParams
- Annotations
- @Since( "3.0.0" )
-
def
getLossType: String
- Definition Classes
- GBTRegressorParams
-
final
def
getMaxBins: Int
- Definition Classes
- DecisionTreeParams
-
final
def
getMaxDepth: Int
- Definition Classes
- DecisionTreeParams
-
final
def
getMaxIter: Int
- Definition Classes
- HasMaxIter
-
final
def
getMinInfoGain: Double
- Definition Classes
- DecisionTreeParams
-
final
def
getMinInstancesPerNode: Int
- Definition Classes
- DecisionTreeParams
-
final
def
getMinWeightFractionPerNode: Double
- Definition Classes
- DecisionTreeParams
-
final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
-
final
def
getSeed: Long
- Definition Classes
- HasSeed
-
final
def
getStepSize: Double
- Definition Classes
- HasStepSize
-
final
def
getSubsamplingRate: Double
- Definition Classes
- TreeEnsembleParams
-
final
def
getValidationIndicatorCol: String
- Definition Classes
- HasValidationIndicatorCol
-
final
def
getValidationTol: Double
- Definition Classes
- GBTParams
- Annotations
- @Since( "2.4.0" )
-
final
def
getWeightCol: String
- Definition Classes
- HasWeightCol
(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.
-
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
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