Skip to content

Latest commit

 

History

History
16 lines (9 loc) · 704 Bytes

README.md

File metadata and controls

16 lines (9 loc) · 704 Bytes

HDDT

Hellinger distance decision tree

This is largely UNTESTED so USE AT YOUR OWN RISK

HDDT have been proposed to work well with imbalanced data [1].

The R file contains functions to create Hellinger distance decision tree (HDDT) given training data. It also provides a function to use a learned decision tree to predict new data.

I am currently using simple lists to store the tree. There are currently no functions to visualize the tree.

I will be happy to get feedback and make improvements, or even better if you have implemented improvements :)

[1] David A. Cieslak , Nitesh V. Chawla , Learning Decision Trees for Unbalanced Data, In European Conference on Machine Learning 2008.