Decision Tree Algorithms in C++

Implementation of three decision tree algorithms in C++. Namely id3, RandomForest and AdaBoost(with id3).

This implementation can handle continous and missing value attributes.

Running

1. Compile the code

   $ g++ -w -o decision_tree decision_tree.cpp

2. Run any one of the algorithms

   $ ./decision_tree <ALGORITHM>

   where ALGORITHM can be one of id3, random_forest, or adaboost.

Dataset Info

Adult Dataset has been used.

I split the data beforehand. datafiles/data.txt contains the training instances, and testfiles/test.txt contains the testing instances.

NOTE: Code was written specifically for this dataset and will require significant changes before running on some other dataset.

Results

Algorithm	Accuracy (%)	Runtime
id3	80.5356	~1min
RandomForest	82.4335	~3mins
AdaBoost	81.3218	~3mins

Additional Notes

I did this project to get a better understanding of the said algorithms, and thus, performance optimizations were not a priority. Coming back to the code after several years, I can see that there are several oppurtunities for improvement.