Machine Learning

This project compares LIME and SHAP for explaining heart disease predictions using the UCI Heart Disease dataset. Several models were built from scratch, with XGBoost achieving the highest accuracy (83.3%). While SHAP produced stable, theory-based feature attributions, LIME offered intuitive, instance-level insights that benefited from an aggregated approach to improve consistency. Together, they highlight how combining interpretability tools can make machine learning in healthcare both accurate and transparent.

To streamline the generation of high-quality multiple-choice questions (MCQs), two sequential pipelines were created: one with a modular design for question/answer and distractor generation, and another with a single unified model. BART was fine-tuned on the SciQ dataset, and the unified model outperformed the modular model on qualitative measures and cosine similarity of correct answers and distractors. Another pipeline was developed to extend the use of the model: given a URL and a topic, it generates an MCQ. A fine-tuned BART-large model successfully generated MCQs beyond the science domain of the SciQ dataset.