object DecisionTreeC45
The DecisionTreeC45 companion object provides factory methods.
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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
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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
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- val drp: (Null, Int, Null, Int, Set[Int])
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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
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