Decision-Tree-CART

Decision tree implementation from scratch

• project folder structure :

  • DecisionTree - contains the implemntation of decision tree
  • Test - contain the classification model build based on top of iris dataset (comparision with sklearn version of decision tree) - no parameter tunning is performed

• Python version : v3.6

• dependency : numpy v1.13.1

output

• Our Model Accuracy : 0.7368421052631579
• SK-Learn Model Accuracy : 0.7631578947368421

Future #todo task:

• Analyse the reson for the performance deviation with sklearn(76 % accuracy) to 73 % accuracy.
• use other performance metric - right now its a raw accuracy number used for comaprision
• test on more dataset fro UCI machine learning repository
• implement tree purning technique to reduce overfitting
• adapt tree for regression by creating differnt mechanism for creating terminal node
• try cross entropy for evaluting the split