Decision trees are fundamental machine learning models known for their interpretability and efficiency. However, standard decision tree algorithms, such as CART and C4.5, predominantly rely on axis-aligned splits, which limit their ability to capture complex feature interactions effectively. This paper introduces a novel approach to decision tree induction that transcends the limitations of axis-aligned splits by employing learnable splitting functions. Our method learns splitting criteria directly from the data, …