Binary C45 Decision Tree
Learn from continuous Independent data
Build Binary Decision Tree based on Info Gain Ratio
Configuration Input
minNumObj   minimum number of instances in a leaf node, used in splitting process
maxLevel    stop learning criteria, either tree's level reachs maxLevel depth or no more split can be done.
Module Files:
- Classify.ecl
- Trees.ecl
Example Files:
- BinaryC45Horizontal.ecl + lymphomaDS.ecl, high dimentional dataset 96 inst x 4026 attribs + class
- BinaryC45Vertical.ecl		, dataset 26733 inst x 100 attribs + class
- BinaryC45Iris.ecl , Iris dataset 150 inst x 4 attribs + class
Merge pull request #177 from DonVitorio/BinaryC45_DT

Binary C45 Decision Tree