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object DecisionTreeC45

The DecisionTreeC45 companion object provides factory methods.

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  1. def apply(x: MatriI, y: VectoI, fn: Strings, k: Int, cn: Strings, hparam: HyperParameter): Unit

    Create a decision tree for the given data matrix and response/classification vector.

    Create a decision tree for the given data matrix and response/classification vector. Takes all integer data (no continuous features).

    x

    the data matrix (features)

    y

    the response/classification vector

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    hparam

    the hyper-parameters for the decision tree

  2. def apply(xy: MatriD, fn: Strings = null, k: Int = 2, cn: Strings = null, conts: Set[Int] = Set [Int] (), hparam: HyperParameter = hp): DecisionTreeC45

    Create a decision tree for the given combined matrix where the last column is the response/classification vector.

    Create a decision tree for the given combined matrix where the last column is the response/classification vector.

    xy

    the combined data matrix (features and response)

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    conts

    the set of feature indices for variables that are treated as continuous

    hparam

    the hyper-parameters for the decision tree

  3. val drp: (Null, Int, Null, Int, Set[Int])
  4. def test(xy: MatriD, fn: Strings, k: Int, cn: Strings, conts: Set[Int] = Set [Int] (), hparam: HyperParameter = hp): DecisionTreeC45

    Test the decision tree on the given dataset passed in as a combined matrix.

    Test the decision tree on the given dataset passed in as a combined matrix.

    xy

    the combined data matrix (features and response)

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    conts

    the set of feature indices for variables that are treated as continuous

    hparam

    the hyper-parameters for the decision tree