GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent (2305.03515v7)

Abstract: Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difficult optimization problem, as it is non-convex and non-differentiable. Therefore, common approaches learn DTs using a greedy growth algorithm that minimizes the impurity locally at each internal node. Unfortunately, this greedy procedure can lead to inaccurate trees. In this paper, we present a novel approach for learning hard, axis-aligned DTs with gradient descent. The proposed method uses backpropagation with a straight-through operator on a dense DT representation, to jointly optimize all tree parameters. Our approach outperforms existing methods on binary classification benchmarks and achieves competitive results for multi-class tasks. The method is available under: https://github.com/s-marton/GradTree

• The paper introduces a novel gradient descent approach that jointly optimizes all decision tree parameters, overcoming the limitations of greedy methods.
• It employs a dense decision tree representation and a straight-through operator to enable efficient, differentiable learning of axis-aligned splits.
• Empirical evaluations reveal superior performance on binary classification and competitive results on multi-class tasks, enhancing model adaptability.

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent

The paper addresses the challenges of optimizing Decision Trees (DTs), especially focusing on the non-convex and non-differentiable nature of the tree learning problem which traditionally relies on greedy algorithms. Traditional approaches like CART and C4.5 are based on localized impurity minimization strategies, which can result in suboptimal tree structures due to the greedy selection of splits at each level. The authors propose a novel method called GradTree, which employs gradient descent to jointly optimize all parameters of a DT in a non-greedy fashion, using a dense DT representation and backpropagation with a straight-through operator.

Key Contributions

1. Dense DT Representation: A central innovation is the dense representation of DTs. Unlike traditional sparse methods, this representation enables gradient-based optimization by converting discrete decisions into a format amenable to continuous optimization. Each potential feature split is represented in a form that allows simultaneous learning, thanks to differentiable approximations for feature index selection and split functionalities.
2. Gradient-Based Optimization: The paper introduces a backpropagation mechanism using a straight-through operator. This technique allows the retention of discrete decision-making in trees while benefiting from the optimization advantages of continuous parameter tuning.
3. Empirical Evaluation: GradTree is evaluated against several prominent methods, including DNDT, GeneticTree, and CART, on binary and multi-class classification tasks. The results indicate that GradTree delivers superior performance on binary classification datasets and competitive results on multi-class datasets. It particularly excels where traditional greedy methods struggle with local optima constraints.
4. Flexibility and Generalization: The approach enhances DT learning by allowing split adjustments and integrating custom loss functions easily within a gradient descent framework. This brings DTs closer to neural networks in terms of trainability and adaptability, opening up possibilities for their use in online learning environments.

The introduction of GradTree signifies a shift in DT learning paradigms, allowing for more robust and potentially superior models while preserving the inherent interpretability of decision structures. By enabling joint optimization of all tree parameters, this approach provides a viable alternative to traditional greedy algorithms, which have long dominated the field. Moreover, the ability to seamlessly integrate with modern ML workflows while maintaining small tree sizes and low susceptibility to overfitting suggests significant practical utility.

Implications and Future Work

The methodological advancements presented in GradTree promise broader implications for the machine learning community, particularly in tasks where model interpretability is crucial. The ability to optimize DTs via gradient descent could lead to new applications, where explainability and precise control over decision boundaries are essential. Future work could explore extending this methodology to ensemble methods, thereby improving the trade-off between interpretability and predictive performance in complex models. Additionally, the paper suggests refining current pruning techniques and learning tree structure dynamically during training to further enhance scalability and efficiency on large and complex datasets.

In conclusion, the paper provides a well-rounded exploration of transitioning DT learning from a traditionally heuristic approach to one bolstered by gradient-based optimization, thus integrating the benefits of interpretability and robustness in a model class that remains highly relevant across diverse applications.