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, 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.

    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. If the checkpoint directory is not set in org.apache.spark.SparkContext, this setting is ignored.

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 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. If the checkpoint directory is not set in org.apache.spark.SparkContext, this setting is ignored.

  10. def clone(): AnyRef

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  11. def copy: Strategy

    Returns a shallow copy of this instance.

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

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  13. def equals(arg0: Any): Boolean

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  14. def finalize(): Unit

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  15. def getAlgo(): Algo.Algo

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

  17. def getCheckpointInterval(): Int

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

    Definition Classes
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  19. def getImpurity(): Impurity

  20. def getMaxBins(): Int

  21. def getMaxDepth(): Int

  22. def getMaxMemoryInMB(): Int

  23. def getMinInfoGain(): Double

  24. def getMinInstancesPerNode(): Int

  25. def getNumClasses(): Int

  26. def getQuantileCalculationStrategy(): QuantileStrategy.QuantileStrategy

  27. def getSubsamplingRate(): Double

  28. def getUseNodeIdCache(): Boolean

  29. def hashCode(): Int

    Definition Classes
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  30. 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.

  31. final def isInstanceOf[T0]: Boolean

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  32. def isMulticlassClassification: Boolean

  33. def isMulticlassWithCategoricalFeatures: Boolean

  34. 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.

  35. 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.

  36. var maxMemoryInMB: Int

    Maximum memory in MB allocated to histogram aggregation.

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

  37. 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.

  38. 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.

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

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  40. final def notify(): Unit

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  41. final def notifyAll(): Unit

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  42. var numClasses: Int

    Number of classes for classification.

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

  43. var quantileCalculationStrategy: QuantileStrategy.QuantileStrategy

    Algorithm for calculating quantiles.

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

  44. def setAlgo(algo: String): Unit

    Sets Algorithm using a String.

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

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

    Sets categoricalFeaturesInfo using a Java Map.

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

  48. def setCheckpointInterval(arg0: Int): Unit

  49. def setImpurity(arg0: Impurity): Unit

  50. def setMaxBins(arg0: Int): Unit

  51. def setMaxDepth(arg0: Int): Unit

  52. def setMaxMemoryInMB(arg0: Int): Unit

  53. def setMinInfoGain(arg0: Double): Unit

  54. def setMinInstancesPerNode(arg0: Int): Unit

  55. def setNumClasses(arg0: Int): Unit

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

  57. def setSubsamplingRate(arg0: Double): Unit

  58. def setUseNodeIdCache(arg0: Boolean): Unit

  59. var subsamplingRate: Double

    Fraction of the training data used for learning decision tree.

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

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  61. def toString(): String

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  62. 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.

  63. final def wait(): Unit

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

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

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