org.apache.spark.mllib.tree.configuration

Strategy

class Strategy extends Serializable

:: Experimental :: Stores all the configuration options for tree construction

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@Experimental()
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Instance Constructors

  1. new Strategy(algo: Algo.Algo, impurity: Impurity, maxDepth: Int, numClasses: Int, maxBins: Int, categoricalFeaturesInfo: Map[Integer, Integer])

    Java-friendly constructor for org.apache.spark.mllib.tree.configuration.Strategy

  2. new Strategy(algo: Algo.Algo, impurity: Impurity, maxDepth: Int, numClasses: Int = 2, maxBins: Int = 32, quantileCalculationStrategy: QuantileStrategy.QuantileStrategy = ..., categoricalFeaturesInfo: Map[Int, Int] = ..., minInstancesPerNode: Int = 1, minInfoGain: Double = 0.0, maxMemoryInMB: Int = 256, subsamplingRate: Double = 1, useNodeIdCache: Boolean = false, checkpointDir: Option[String] = scala.None, checkpointInterval: Int = 10)

    algo

    Learning goal. Supported: org.apache.spark.mllib.tree.configuration.Algo.Classification, org.apache.spark.mllib.tree.configuration.Algo.Regression

    impurity

    Criterion used for information gain calculation. Supported for Classification: org.apache.spark.mllib.tree.impurity.Gini, org.apache.spark.mllib.tree.impurity.Entropy. Supported for Regression: org.apache.spark.mllib.tree.impurity.Variance.

    maxDepth

    Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

    numClasses

    Number of classes for classification. (Ignored for regression.) Default value is 2 (binary classification).

    maxBins

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity.

    quantileCalculationStrategy

    Algorithm for calculating quantiles. Supported: org.apache.spark.mllib.tree.configuration.QuantileStrategy.Sort

    categoricalFeaturesInfo

    A map storing information about the categorical variables and the number of discrete values they take. For example, an entry (n -> k) implies the feature n is categorical with k categories 0, 1, 2, ... , k-1. It's important to note that features are zero-indexed.

    minInstancesPerNode

    Minimum number of instances each child must have after split. Default value is 1. If a split cause left or right child to have less than minInstancesPerNode, this split will not be considered as a valid split.

    minInfoGain

    Minimum information gain a split must get. Default value is 0.0. If a split has less information gain than minInfoGain, this split will not be considered as a valid split.

    maxMemoryInMB

    Maximum memory in MB allocated to histogram aggregation. Default value is 256 MB.

    subsamplingRate

    Fraction of the training data used for learning decision tree.

    useNodeIdCache

    If this is true, instead of passing trees to executors, the algorithm will maintain a separate RDD of node Id cache for each row.

    checkpointDir

    If the node Id cache is used, it will help to checkpoint the node Id cache periodically. This is the checkpoint directory to be used for the node Id cache.

    checkpointInterval

    How often to checkpoint when the node Id cache gets updated. E.g. 10 means that the cache will get checkpointed every 10 updates.

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. var algo: Algo.Algo

    Learning goal.

  7. final def asInstanceOf[T0]: T0

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  8. var categoricalFeaturesInfo: Map[Int, Int]

    A map storing information about the categorical variables and the number of discrete values they take.

    A map storing information about the categorical variables and the number of discrete values they take. For example, an entry (n -> k) implies the feature n is categorical with k categories 0, 1, 2, ... , k-1. It's important to note that features are zero-indexed.

  9. var checkpointDir: Option[String]

    If the node Id cache is used, it will help to checkpoint the node Id cache periodically.

    If the node Id cache is used, it will help to checkpoint the node Id cache periodically. This is the checkpoint directory to be used for the node Id cache.

  10. var checkpointInterval: Int

    How often to checkpoint when the node Id cache gets updated.

    How often to checkpoint when the node Id cache gets updated. E.g. 10 means that the cache will get checkpointed every 10 updates.

  11. def clone(): AnyRef

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  12. def copy: Strategy

    Returns a shallow copy of this instance.

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

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  14. def equals(arg0: Any): Boolean

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  15. def finalize(): Unit

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  16. def getAlgo(): Algo.Algo

  17. def getCategoricalFeaturesInfo(): Map[Int, Int]

  18. def getCheckpointDir(): Option[String]

  19. def getCheckpointInterval(): Int

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

    Definition Classes
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  21. def getImpurity(): Impurity

  22. def getMaxBins(): Int

  23. def getMaxDepth(): Int

  24. def getMaxMemoryInMB(): Int

  25. def getMinInfoGain(): Double

  26. def getMinInstancesPerNode(): Int

  27. def getNumClasses(): Int

  28. def getQuantileCalculationStrategy(): QuantileStrategy.QuantileStrategy

  29. def getSubsamplingRate(): Double

  30. def getUseNodeIdCache(): Boolean

  31. def hashCode(): Int

    Definition Classes
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  32. var impurity: Impurity

    Criterion used for information gain calculation.

    Criterion used for information gain calculation. Supported for Classification: org.apache.spark.mllib.tree.impurity.Gini, org.apache.spark.mllib.tree.impurity.Entropy. Supported for Regression: org.apache.spark.mllib.tree.impurity.Variance.

  33. final def isInstanceOf[T0]: Boolean

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  34. def isMulticlassClassification: Boolean

  35. def isMulticlassWithCategoricalFeatures: Boolean

  36. var maxBins: Int

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity.

  37. var maxDepth: Int

    Maximum depth of the tree.

    Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

  38. var maxMemoryInMB: Int

    Maximum memory in MB allocated to histogram aggregation.

    Maximum memory in MB allocated to histogram aggregation. Default value is 256 MB.

  39. var minInfoGain: Double

    Minimum information gain a split must get.

    Minimum information gain a split must get. Default value is 0.0. If a split has less information gain than minInfoGain, this split will not be considered as a valid split.

  40. var minInstancesPerNode: Int

    Minimum number of instances each child must have after split.

    Minimum number of instances each child must have after split. Default value is 1. If a split cause left or right child to have less than minInstancesPerNode, this split will not be considered as a valid split.

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

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  42. final def notify(): Unit

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  43. final def notifyAll(): Unit

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  44. var numClasses: Int

    Number of classes for classification.

    Number of classes for classification. (Ignored for regression.) Default value is 2 (binary classification).

  45. var quantileCalculationStrategy: QuantileStrategy.QuantileStrategy

    Algorithm for calculating quantiles.

    Algorithm for calculating quantiles. Supported: org.apache.spark.mllib.tree.configuration.QuantileStrategy.Sort

  46. def setAlgo(algo: String): Unit

    Sets Algorithm using a String.

  47. def setAlgo(arg0: Algo.Algo): Unit

  48. def setCategoricalFeaturesInfo(categoricalFeaturesInfo: Map[Integer, Integer]): Unit

    Sets categoricalFeaturesInfo using a Java Map.

  49. def setCategoricalFeaturesInfo(arg0: Map[Int, Int]): Unit

  50. def setCheckpointDir(arg0: Option[String]): Unit

  51. def setCheckpointInterval(arg0: Int): Unit

  52. def setImpurity(arg0: Impurity): Unit

  53. def setMaxBins(arg0: Int): Unit

  54. def setMaxDepth(arg0: Int): Unit

  55. def setMaxMemoryInMB(arg0: Int): Unit

  56. def setMinInfoGain(arg0: Double): Unit

  57. def setMinInstancesPerNode(arg0: Int): Unit

  58. def setNumClasses(arg0: Int): Unit

  59. def setQuantileCalculationStrategy(arg0: QuantileStrategy.QuantileStrategy): Unit

  60. def setSubsamplingRate(arg0: Double): Unit

  61. def setUseNodeIdCache(arg0: Boolean): Unit

  62. var subsamplingRate: Double

    Fraction of the training data used for learning decision tree.

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

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  64. def toString(): String

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  65. var useNodeIdCache: Boolean

    If this is true, instead of passing trees to executors, the algorithm will maintain a separate RDD of node Id cache for each row.

  66. final def wait(): Unit

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

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

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