main webpage W Topic Computer Science Zenodo (CERN European Organization for Nuclear Research) Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions 2025 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 … Article Open Computer Science Open Article Page

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Study of computation Computer science is the study of computation, information, and automation. Computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Critical Symbolic Virtual Narrative Open Computer Science Open Article Page

Exploring foci of: Zenodo (CERN European Organization for Nuclear Research) Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions 2025 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, … Open Article Page

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Explore Computer Science W Topic Computer Science Zenodo (CERN European Organization for Nuclear Research) Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions 2025 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 … Article Critical Symbolic Virtual Narrative Open Computer Science Open Article Page