Class/Object

au.csiro.variantspark.algo

DecisionTree

Related Docs: object DecisionTree | package algo

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class DecisionTree[V] extends Logging with Prof

Class for the Decision tree model

Specify the 'params' using the au.csiro.variantspark.algo.DecisionTreeParams

val maxDepth = 5
val minNodeSize = 10
val seed = 1
val randomizeEquality = false

val params = DecisionTreeParams(maxDepth, minNodeSize, seed, randomizeEquality)
val model = DecisionTree(params)
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Instance Constructors

  1. new DecisionTree(params: DecisionTreeParams = DecisionTreeParams())(implicit canSplit: CanSplit[V])

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Value Members

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

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  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. def batchTrain(indexedData: RDD[(V, Long)], dataType: VariableType, labels: Array[Int], nvarFraction: Double, sample: Seq[Sample]): Seq[DecisionTreeModel[V]]

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    Trains all the trees for specified samples at the same time

    Trains all the trees for specified samples at the same time

    returns

    Returns a Sequence of au.csiro.variantspark.algo.DecisionTreeModel classes containing the dataset

  6. def buildSplit(indexedData: RDD[(V, Long)], subsets: List[SubsetInfo], br_splitter: Broadcast[VariableSplitter[V]], treeLevel: Int): List[DecisionTreeNode]

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    Builds (recursively) the decision tree level by level

    Builds (recursively) the decision tree level by level

    returns

    Returns a subset of the splits

  7. def clone(): AnyRef

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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 finalize(): Unit

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  11. def findBestSplitsAndSubsets(indexedData: RDD[(V, Long)], subsetsToSplit: List[SubsetInfo], br_splitter: Broadcast[VariableSplitter[V]]): (Array[VarSplitInfo], List[SubsetInfo])

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    Finds the best split using the au.csiro.variantspark.algo.DecisionTree class's findBestSplits function then broadcast to the bestSplits variable

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. def isTraceEnabled(): Boolean

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  16. def log: Logger

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  17. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  18. def logDebug(msg: ⇒ String): Unit

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  19. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  20. def logError(msg: ⇒ String): Unit

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  21. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  22. def logInfo(msg: ⇒ String): Unit

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  23. def logName: String

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  24. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  25. def logTrace(msg: ⇒ String): Unit

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  26. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  27. def logWarning(msg: ⇒ String): Unit

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

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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  31. val params: DecisionTreeParams

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  32. var prevStartTime: Long

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  33. lazy val profEnable: Boolean

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  34. def profIt[R](msg: ⇒ String)(f: ⇒ R): R

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  35. def profPoint(msg: ⇒ String): Unit

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  36. def profReset(): Unit

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  37. implicit lazy val rnd: XorShift1024StarRandomGenerator

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  38. def run(indexedData: RDD[(V, Long)], dataType: VariableType, labels: Array[Int], nvarFraction: Double, sample: Sample): DecisionTreeModel[V]

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    Alternative run function

  39. def run(data: RDD[V], dataType: VariableType, labels: Array[Int]): DecisionTreeModel[V]

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    Basic run operation taking the in the data, the type, and the labels

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

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  41. def toString(): String

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  42. final def wait(): Unit

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

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

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