object DecisionTreeC45
DecisionTreeC45
is the companion object provides factory methods.
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def
apply(xy: MatriD, isCont: Array[Boolean], fn: Strings = null, k: Int = 2, cn: Strings = null, vc: Array[Int] = null, td: Int = 0): 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 data vectors along with their classifications stored as rows of a matrix
- isCont
Boolean
value to indicate whether according feature is continuous- fn
the names for all features/variables
- k
the number of classes
- cn
the names for all classes
- vc
the value count array indicating number of distinct values per feature
- td
the maximum tree depth to allow (defaults to 0 => number of features, -1 no constraint
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def
test(xy: MatriD, fn: Strings, isCont: Array[Boolean], k: Int = 2, cn: Strings = null, vc: Array[Int] = null, td: Int = 0): 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 data vectors along with their classifications stored as rows of a matrix
- fn
the names for all features/variables
- isCont
Boolean
value to indicate whether according feature is continuous- k
the number of classes
- cn
the names for all classes
- vc
the value count array indicating number of distinct values per feature
- td
the maximum tree depth to allow (defaults to 0 => number of features, -1 no constraint
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