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(featureNames: Array[String]): Map[String, Integer]

    Get importance of each feature based on weight only (number of splits), with specified feature names.

    Get importance of each feature based on weight only (number of splits), with specified feature names.

    returns

      featureScoreMap key: feature name, value: feature importance score

    Annotations
      @throws( classOf[XGBoostError] )

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

    Get importance of each feature based on weight only (number of splits)

    Get importance of each feature based on weight only (number of splits)

    returns

      featureScoreMap key: feature index, value: feature importance score

    Annotations
      @throws( classOf[XGBoostError] )

16. #### def getModelDump(featureNames: Array[String], withStats: Boolean, format: String): Array[String]

17. #### def getModelDump(featureNames: Array[String]): Array[String]

    Dump model as Array of string with specified feature names.

    Dump model as Array of string with specified feature names.

    featureNames

      Names of features.

    Annotations
      @throws( classOf[XGBoostError] )

18. #### 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] )

19. #### def getScore(featureNames: Array[String], importanceType: String): Map[String, Double]

    Get importance of each feature based on information gain or cover , with specified feature names.

    Get importance of each feature based on information gain or cover , with specified feature names. Supported: ["gain, "cover", "total_gain", "total_cover"]

    returns

      featureScoreMap key: feature name, value: feature importance score

    Annotations
      @throws( classOf[XGBoostError] )

20. #### def getScore(featureMap: String, importanceType: String): Map[String, Double]

    Get importance of each feature based on information gain or cover Supported: ["gain, "cover", "total_gain", "total_cover"]

    Get importance of each feature based on information gain or cover Supported: ["gain, "cover", "total_gain", "total_cover"]

    returns

      featureScoreMap key: feature index, value: feature importance score

    Annotations
      @throws( classOf[XGBoostError] )

21. #### def getVersion: Int

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

    Definition Classes
      AnyRef → Any

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

    Definition Classes
      Any

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

    Definition Classes
      AnyRef

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

    Definition Classes
      AnyRef

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

    Definition Classes
      AnyRef

27. #### 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] )

28. #### 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

29. #### 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

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

    Definition Classes
      Booster → KryoSerializable

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

    save model to Output stream

    save model to Output stream

    out

      Output stream

    Annotations
      @throws( classOf[XGBoostError] )

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

    save model to modelPath

    save model to modelPath

    modelPath

      model path

    Annotations
      @throws( classOf[XGBoostError] )

33. #### 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] )

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

    set parameters

    set parameters

    params

      parameters key-value map

    Annotations
      @throws( classOf[XGBoostError] )

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

    Definition Classes
      AnyRef

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

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

    Definition Classes
      AnyRef → Any

38. #### 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] )

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

    Update (one iteration)

    Update (one iteration)

    dtrain

      training data

    iter

      current iteration number

    Annotations
      @throws( classOf[XGBoostError] )

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

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

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

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

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

    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

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

    Definition Classes
      Booster → KryoSerializable

### Inherited from KryoSerializable

### Inherited from Serializable

### Inherited from AnyRef

### Inherited from Any

### Ungrouped