Build a Decision Tree by splitting based on GINI index. Also outputs the GINI values to explain why the algo chose some attribute.
Input:
Note: The last column must be the class identifier
python3 huntDT_driver.py -v --table_csv table.csvOutput:
Note: Each node in the tree is represented as (attribute column number that this node was previously splitted: attribute value). That's why the root node is always (0: None).
Gini split for 0: 0.48
Gini split for 1: 0.16250000000000003
Gini split for 2: 0.4789285714285715
Choose att 1
Gini split for 0: 0.375
Gini split for 2: 0.0
Choose att 2
Gini split for 0: 0.0
Choose att 0
Gini split for 0: 0.0
Choose att 0
Gini split for 0: 0.0
Choose att 0
Gini split for 0: 0.21428571428571433
Gini split for 2: 0.16666666666666666
Choose att 2
Gini split for 0: 0.3333333333333333
Choose att 0
Gini split for 0: 0.0
Choose att 0
Gini split for 0: 0.0
Choose att 0
Gini split for 0: 0.0
Gini split for 2: 0.0
Choose att 0
Gini split for 2: 0.0
Choose att 2
Gini split for 2: 0.0
Choose att 2
|-> (0: None)
|-> (1: High)
|-> (2: Poor)
|-> (0: F)
|-> (0: M)
|-> (B: 1)
|-> (A: 0)
|-> (2: Fair)
|-> (0: F)
|-> (0: M)
|-> (B: 0)
|-> (A: 1)
|-> (2: Excellent)
|-> (0: F)
|-> (0: M)
|-> (B: 2)
|-> (A: 0)
|-> (1: Low)
|-> (2: Poor)
|-> (0: F)
|-> (B: 1)
|-> (A: 1)
|-> (0: M)
|-> (B: 1)
|-> (A: 0)
|-> (2: Fair)
|-> (0: F)
|-> (B: 2)
|-> (A: 0)
|-> (0: M)
|-> (2: Excellent)
|-> (0: F)
|-> (B: 3)
|-> (A: 0)
|-> (0: M)
|-> (1: Medium)
|-> (0: F)
|-> (2: Poor)
|-> (2: Fair)
|-> (B: 0)
|-> (A: 2)
|-> (2: Excellent)
|-> (B: 0)
|-> (A: 1)
|-> (0: M)
|-> (2: Poor)
|-> (B: 0)
|-> (A: 3)
|-> (2: Fair)
|-> (2: Excellent)
|-> (B: 0)
|-> (A: 2)