Decision Tree

Decision trees are a fundamental machine learning model aiming to create interpretable predictive models by recursively partitioning data based on feature values. Current research emphasizes improving their accuracy, efficiency, and robustness, particularly within ensemble methods like random forests and gradient boosted trees, as well as exploring novel architectures like auto-regressive decision trees and their application in areas such as natural language processing. This ongoing work addresses challenges like vulnerability to adversarial attacks, handling noisy data, and optimizing for both accuracy and interpretability, impacting diverse fields from fraud detection to cybersecurity and beyond.