The final project in Ben-gurion university of the negev Machine learning course. The task was to implement a ML solution published in recent years in top-ranking conventions, then to evaluate it and analyze the results.
In this project, we choose to implement ECSDT – Ensemble of Example-Dependent Cost-Sensitive Decision Trees. (paper included in the repo) "Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015). Ensemble of example-dependent cost-sensitive decision trees. arXiv preprint arXiv:1505.04637."
The implementation itself is in ECSDT.py, the code to run the experiment is in the Main.py and the functions to load and process the the datasets are in the data_loader.py.
Our final report is also included with a detail explanation of the algorithm and implementation and analysis. The work was done with Inbal Roshanski