org.apache.spark.mllib.tree.model
GradientBoostedTreesModel
Companion object GradientBoostedTreesModel
class GradientBoostedTreesModel extends TreeEnsembleModel with Saveable
Represents a gradient boosted trees model.
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- @Since( "1.2.0" )
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- GradientBoostedTreesModel
- Saveable
- TreeEnsembleModel
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Instance Constructors
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new
GradientBoostedTreesModel(algo: Algo, trees: Array[DecisionTreeModel], treeWeights: Array[Double])
- algo
algorithm for the ensemble model, either Classification or Regression
- trees
tree ensembles
- treeWeights
tree ensemble weights
- Annotations
- @Since( "1.2.0" )
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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val
algo: Algo
- Definition Classes
- GradientBoostedTreesModel → TreeEnsembleModel
- Annotations
- @Since( "1.2.0" )
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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val
combiningStrategy: EnsembleCombiningStrategy
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- protected
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- TreeEnsembleModel
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
evaluateEachIteration(data: RDD[LabeledPoint], loss: Loss): 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.
- data
- loss
evaluation metric.
- returns
an array with index i having the losses or errors for the ensemble containing the first i+1 trees
- Annotations
- @Since( "1.4.0" )
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def
finalize(): Unit
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def
formatVersion: String
Current version of model save/load format.
Current version of model save/load format.
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- protected
- Definition Classes
- GradientBoostedTreesModel → Saveable
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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isInstanceOf[T0]: Boolean
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ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
numTrees: Int
Get number of trees in ensemble.
Get number of trees in ensemble.
- Definition Classes
- TreeEnsembleModel
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def
predict(features: JavaRDD[Vector]): JavaRDD[Double]
Java-friendly version of
org.apache.spark.mllib.tree.model.TreeEnsembleModel.predict
.Java-friendly version of
org.apache.spark.mllib.tree.model.TreeEnsembleModel.predict
.- Definition Classes
- TreeEnsembleModel
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def
predict(features: RDD[Vector]): RDD[Double]
Predict values for the given data set.
Predict values for the given data set.
- features
RDD representing data points to be predicted
- returns
RDD[Double] where each entry contains the corresponding prediction
- Definition Classes
- TreeEnsembleModel
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def
predict(features: Vector): Double
Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
- features
array representing a single data point
- returns
predicted category from the trained model
- Definition Classes
- TreeEnsembleModel
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def
save(sc: SparkContext, path: String): Unit
- sc
Spark context used to save model data.
- path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.
- Definition Classes
- GradientBoostedTreesModel → Saveable
- Annotations
- @Since( "1.3.0" )
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toDebugString: String
Print the full model to a string.
Print the full model to a string.
- Definition Classes
- TreeEnsembleModel
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def
toString(): String
Print a summary of the model.
Print a summary of the model.
- Definition Classes
- TreeEnsembleModel → AnyRef → Any
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def
totalNumNodes: Int
Get total number of nodes, summed over all trees in the ensemble.
Get total number of nodes, summed over all trees in the ensemble.
- Definition Classes
- TreeEnsembleModel
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val
treeWeights: Array[Double]
- Definition Classes
- GradientBoostedTreesModel → TreeEnsembleModel
- Annotations
- @Since( "1.2.0" )
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val
trees: Array[DecisionTreeModel]
- Definition Classes
- GradientBoostedTreesModel → TreeEnsembleModel
- Annotations
- @Since( "1.2.0" )
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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def
wait(arg0: Long): Unit
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