Making Explainable Decision Trees Faster & More Accessible
Balancing model accuracy with interpretability remains a central challenge in machine learning, particularly for classification tasks involving large datasets and continuous variables. This work examined how optimal sparse decision tree frameworks can be made more efficient and accessible without sacrificing their explainability or theoretical guarantees. The research was presented at the Graduate Poster Exhibition during… continue reading
