TODO: Implement gini index using the method of intropy for the better results and accuracy

Implementation of decision tree in python dataset used : bank_note dataset with five attributes.

link: https://www.openml.org/d/1462

links: https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/

Linkes for confusion matrix: http://chem-eng.utoronto.ca/~datamining/dmc/model_evaluation_c.htm

creating the binary decision tree is actually dividing the input space

A greedy approach is used to divide the space called recursivebinary spliting.

split with best cost is selected.

Regression: The cost function that is minimized to choose split point is the sum squared error across all training samples that fall within the rectangle.

Dataset used : BankNote dataset.

Zero rule classification: This predicts the majority class irrespective of the classifier.

Gini index: is the name of cost function to split the dataset.