ml.dmlc.xgboost4j.scala

# Booster

###
Related Doc: package scala

#### class Booster extends Serializable with KryoSerializable

Booster for xgboost, this is a model API that support interactive build of a XGBoost Model

DEVELOPER WARNING: A Java Booster must not be shared by more than one Scala Booster

Linear Supertypes
KryoSerializable, Serializable, Serializable, AnyRef, Any
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1. Booster
2. KryoSerializable
3. Serializable
4. Serializable
5. AnyRef
6. Any

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### Value Members

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

   Definition Classes
     AnyRef → Any

2. #### final def ##(): Int

   Definition Classes
     AnyRef → Any

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

   Definition Classes
     AnyRef → Any

4. #### final def asInstanceOf[T0]: T0

   Definition Classes
     Any

5. #### def boost(dtrain: DMatrix, grad: Array[Float], hess: Array[Float]): Unit

   update with give grad and hess

   update with give grad and hess

   dtrain

     training data

   grad

     first order of gradient

   hess

     seconde order of gradient

   Annotations
     @throws( classOf[XGBoostError] )

6. #### def clone(): AnyRef

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

7. #### def dispose: Unit

   Dispose the booster when it is no longer needed

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

   Definition Classes
     AnyRef

9. #### def equals(arg0: Any): Boolean

   Definition Classes
     AnyRef → Any

10. #### def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: EvalTrait): String

    evaluate with given customized Evaluation class

    evaluate with given customized Evaluation class

    evalMatrixs

      evaluation matrix

    evalNames

      evaluation names

    eval

      custom evaluator

    returns

      eval information

    Annotations
      @throws( classOf[XGBoostError] )

11. #### def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], iter: Int): String

    evaluate with given dmatrixs.

    evaluate with given dmatrixs.

    evalMatrixs

      dmatrixs for evaluation

    evalNames

      name for eval dmatrixs, used for check results

    iter

      current eval iteration

    returns

      eval information

    Annotations
      @throws( classOf[XGBoostError] )

12. #### def finalize(): Unit

    Definition Classes
      Booster → AnyRef

13. #### final def getClass(): Class[_]

    Definition Classes
      AnyRef → Any

14. #### def getFeatureScore(featureMap: String = null): Map[String, Integer]

    Get importance of each feature

    Get importance of each feature

    returns

      featureMap key: feature index, value: feature importance score

    Annotations
      @throws( classOf[XGBoostError] )

15. #### def getModelDump(featureMap: String = null, withStats: Boolean = false, format: String = "text"): Array[String]

    Dump model as Array of string

    Dump model as Array of string

    featureMap

      featureMap file

    withStats

      bool Controls whether the split statistics are output.

    Annotations
      @throws( classOf[XGBoostError] )

16. #### def hashCode(): Int

    Definition Classes
      AnyRef → Any

17. #### final def isInstanceOf[T0]: Boolean

    Definition Classes
      Any

18. #### final def ne(arg0: AnyRef): Boolean

    Definition Classes
      AnyRef

19. #### final def notify(): Unit

    Definition Classes
      AnyRef

20. #### final def notifyAll(): Unit

    Definition Classes
      AnyRef

21. #### def predict(data: DMatrix, outPutMargin: Boolean = false, treeLimit: Int = 0): Array[Array[Float]]

    Predict with data

    Predict with data

    data

      dmatrix storing the input

    outPutMargin

      Whether to output the raw untransformed margin value.

    treeLimit

      Limit number of trees in the prediction; defaults to 0 (use all trees).

    returns

      predict result

    Annotations
      @throws( classOf[XGBoostError] )

22. #### def predictContrib(data: DMatrix, treeLimit: Int = 0): Array[Array[Float]]

    Output feature contributions toward predictions of given data

    Output feature contributions toward predictions of given data

    data

      dmatrix storing the input

    treeLimit

      Limit number of trees in the prediction; defaults to 0 (use all trees).

    returns

      The feature contributions and bias.

    Annotations
      @throws( classOf[XGBoostError] )
    Exceptions thrown

      `XGBoostError` native error

23. #### def predictLeaf(data: DMatrix, treeLimit: Int = 0): Array[Array[Float]]

    Predict the leaf indices

    Predict the leaf indices

    data

      dmatrix storing the input

    treeLimit

      Limit number of trees in the prediction; defaults to 0 (use all trees).

    returns

      predict result

    Annotations
      @throws( classOf[XGBoostError] )
    Exceptions thrown

      `XGBoostError` native error

24. #### def read(kryo: Kryo, input: Input): Unit

    Definition Classes
      Booster → KryoSerializable

25. #### def saveModel(out: OutputStream): Unit

    save model to Output stream

    save model to Output stream

    out

      Output stream

    Annotations
      @throws( classOf[XGBoostError] )

26. #### def saveModel(modelPath: String): Unit

    save model to modelPath

    save model to modelPath

    modelPath

      model path

    Annotations
      @throws( classOf[XGBoostError] )

27. #### def setParam(key: String, value: AnyRef): Unit

    Set parameter to the Booster.

    Set parameter to the Booster.

    key

      param name

    value

      param value

    Annotations
      @throws( classOf[XGBoostError] )

28. #### def setParams(params: Map[String, AnyRef]): Unit

    set parameters

    set parameters

    params

      parameters key-value map

    Annotations
      @throws( classOf[XGBoostError] )

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

    Definition Classes
      AnyRef

30. #### def toByteArray: Array[Byte]

31. #### def toString(): String

    Definition Classes
      AnyRef → Any

32. #### def update(dtrain: DMatrix, obj: ObjectiveTrait): Unit

    update with customize obj func

    update with customize obj func

    dtrain

      training data

    obj

      customized objective class

    Annotations
      @throws( classOf[XGBoostError] )

33. #### def update(dtrain: DMatrix, iter: Int): Unit

    Update (one iteration)

    Update (one iteration)

    dtrain

      training data

    iter

      current iteration number

    Annotations
      @throws( classOf[XGBoostError] )

34. #### final def wait(): Unit

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

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

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

36. #### final def wait(arg0: Long): Unit

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

37. #### def write(kryo: Kryo, output: Output): Unit

    Definition Classes
      Booster → KryoSerializable

### Inherited from KryoSerializable

### Inherited from Serializable

### Inherited from AnyRef

### Inherited from Any

### Ungrouped