Oblique Decision Tree

Oblique decision trees, unlike traditional decision trees, use linear combinations of features to create more complex decision boundaries, improving their ability to model intricate data relationships. Current research focuses on developing efficient training methods, such as gradient descent techniques applied to differentiable tree representations, and on integrating oblique trees into ensemble methods like random forests to enhance performance and address issues like fairness and generalization. These advancements are significant because they improve the accuracy and efficiency of oblique decision trees, making them valuable tools for various machine learning tasks, including classification, regression, and even reinforcement learning.