GBTClassifierModel

Instance Constructors

new GBTClassifierModel(trees: Seq[DecisionTreeRegressionModel], treeWeights: Seq[Double], numFeatures: Int, thresholds: Option[Array[Double]] = None)

trees
trees in the gradient boost model
treeWeights
weights of each tree
numFeatures
number of features

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def apply(features: Vector): Double

Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.
Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.
features
feature vector
returns
prediction

Definition Classes
ClassificationModel
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def inputSchema: StructType

Definition Classes
ProbabilisticClassificationModel → Model
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def margin(features: Vector): Double

Raw prediction for the positive class.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numClasses: Int

Number of classes this model predicts.
Number of classes this model predicts.
2 indicates this is a binary classification model. Greater than 2 indicates a multinomial classifier.

Definition Classes
GBTClassifierModel → ProbabilisticClassificationModel
val numFeatures: Int

number of features
number of features

Definition Classes
GBTClassifierModel → ProbabilisticClassificationModel
def numTrees: Int

Number of trees in the ensemble
Number of trees in the ensemble

Definition Classes
TreeEnsemble
def outputSchema: StructType

Definition Classes
ProbabilisticClassificationModel → Model
def predict(features: Vector): Double

Predict class based on feature vector.
Predict class based on feature vector.
features
feature vector
returns
predicted class or probability

Definition Classes
GBTClassifierModel → ProbabilisticClassificationModel → ClassificationModel
def predictProbabilities(features: Vector): Vector

Definition Classes
ProbabilisticClassificationModel
def predictRaw(features: Vector): Vector

Definition Classes
GBTClassifierModel → ProbabilisticClassificationModel
def predictWithProbability(features: Vector): (Double, Double)

Definition Classes
ProbabilisticClassificationModel
def probabilityToPrediction(probability: Vector): Double

Definition Classes
ProbabilisticClassificationModel
def probabilityToPredictionIndex(probability: Vector): Int

Definition Classes
ProbabilisticClassificationModel
def rawToPrediction(raw: Vector): Double

Definition Classes
ProbabilisticClassificationModel
def rawToProbability(raw: Vector): Vector

Definition Classes
ProbabilisticClassificationModel
def rawToProbabilityInPlace(raw: Vector): Vector

Definition Classes
GBTClassifierModel → ProbabilisticClassificationModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val thresholds: Option[Array[Double]]

Definition Classes
GBTClassifierModel → ProbabilisticClassificationModel
val treeWeights: Seq[Double]

weights of each tree
weights of each tree

Definition Classes
GBTClassifierModel → TreeEnsemble
val trees: Seq[DecisionTreeRegressionModel]

trees in the gradient boost model
trees in the gradient boost model

Definition Classes
GBTClassifierModel → TreeEnsemble
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( ... )

Related Docs: object GBTClassifierModel | package classification

case class GBTClassifierModel(trees: Seq[DecisionTreeRegressionModel], treeWeights: Seq[Double], numFeatures: Int, thresholds: Option[Array[Double]] = None) extends ProbabilisticClassificationModel with TreeEnsemble with Serializable with Product

Instance Constructors

new GBTClassifierModel(trees: Seq[DecisionTreeRegressionModel], treeWeights: Seq[Double], numFeatures: Int, thresholds: Option[Array[Double]] = None)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def apply(features: Vector): Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def inputSchema: StructType

final def isInstanceOf[T0]: Boolean

def margin(features: Vector): Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numClasses: Int

val numFeatures: Int

def numTrees: Int

def outputSchema: StructType

def predict(features: Vector): Double

def predictProbabilities(features: Vector): Vector

def predictRaw(features: Vector): Vector

def predictWithProbability(features: Vector): (Double, Double)

def probabilityToPrediction(probability: Vector): Double

def probabilityToPredictionIndex(probability: Vector): Int

def rawToPrediction(raw: Vector): Double

def rawToProbability(raw: Vector): Vector

def rawToProbabilityInPlace(raw: Vector): Vector

final def synchronized[T0](arg0: ⇒ T0): T0

val thresholds: Option[Array[Double]]

val treeWeights: Seq[Double]

val trees: Seq[DecisionTreeRegressionModel]

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from Serializable

Inherited from Serializable

Inherited from TreeEnsemble

Inherited from ProbabilisticClassificationModel

Inherited from ClassificationModel

Inherited from Model

Inherited from AnyRef

Inherited from Any

Ungrouped