Class

com.microsoft.ml.spark.lightgbm

LightGBMBooster

Related Doc: package lightgbm

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class LightGBMBooster extends Serializable

Represents a LightGBM Booster learner

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@SerialVersionUID()
Linear Supertypes
Serializable, Serializable, AnyRef, Any
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Visibility
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Instance Constructors

  1. new LightGBMBooster(model: String)

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    model

    The string serialized representation of the learner

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. lazy val boosterHandler: BoosterHandler

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    Transient variable containing local machine's pointer to native booster

  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( ... )

  7. def dumpModel(session: SparkSession, filename: String, overwrite: Boolean): Unit

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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def featuresShap(features: Vector): Array[Double]

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  11. def finalize(): Unit

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    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

  12. final def getClass(): Class[_]

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  13. def getFeatureImportances(importanceType: String): Array[Double]

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    Calls into LightGBM to retrieve the feature importances.

    Calls into LightGBM to retrieve the feature importances.

    importanceType

    Can be "split" or "gain"

    returns

    The feature importance values as an array.

  14. def hashCode(): Int

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    Definition Classes
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  15. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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  16. val model: String

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    The string serialized representation of the learner

  17. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. lazy val numClasses: Int

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  21. lazy val numFeatures: Int

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  22. lazy val numIterations: Int

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  23. lazy val numModelPerIteration: Int

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  24. lazy val numTotalModel: Int

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  25. def predictForCSR(sparseVector: SparseVector, kind: Int, dataLengthLongPtr: SWIGTYPE_p_long_long, dataOutPtr: SWIGTYPE_p_double): Unit

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    Attributes
    protected

  26. def predictForMat(row: Array[Double], kind: Int, dataLengthLongPtr: SWIGTYPE_p_long_long, dataOutPtr: SWIGTYPE_p_double): Unit

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    Attributes
    protected

  27. def predictLeaf(features: Vector): Array[Double]

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  28. def saveNativeModel(session: SparkSession, filename: String, overwrite: Boolean): Unit

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  29. def score(features: Vector, raw: Boolean, classification: Boolean): Array[Double]

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

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  31. def toString(): String

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  32. final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

  34. final def wait(arg0: Long): Unit

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