This repository is my implementation of Decision Trees from scratch. No use of Machine Learning libraries. I even skipped on Pandas and Numpy. So, basically everything is from scratch.

Sure the algorithm can be tuned at a lot of places for better results. But, the point of this implementation is to get a clear idea about how the algorithm works :)

That said, I plan on improving my code as I learn new techniques.

Using KCrossValidation using 3 folds on the banknote dataset, this is the output

NOTE: Data preprocessing is not taken much into account

1. Clone the repo

git clone https://github.com/iArunava/Decision-Trees.git

2. Move into the cloned repo

cd Decision-Trees

3. Run the main.py file

python3 main.py

4. You can pass the max_depth= and min_size= as arguments while calling the main.py file.

python3 main.py 12 10

NOTE: max_depth is the first argument and min_size is the second argument.

NOTE: Missing one argument will set default arguments for max_depth and min_size with a warning shown.

NOTE: The default parameters for max_depth is 9 and min_size is 8.

Distributed under MIT. Have fun!