NBDT: Neural-Backed Decision Trees (2004.00221v3)

Abstract: Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at https://github.com/alvinwan/neural-backed-decision-trees.

• The paper proposes a hybrid architecture that replaces a neural network’s final layer with a differentiable decision tree to combine high accuracy with clear interpretability.
• The paper introduces a tree supervision loss that optimizes accuracy while preserving the decision-making transparency, improving performance by up to 2%.
• The paper demonstrates enhanced generalization on benchmarks, achieving up to 16% better performance on unseen classes and 15% increased accuracy on ImageNet.

An Overview of Neural-Backed Decision Trees (NBDTs)

The paper "NBDT: Neural-Backed Decision Tree" introduces Neural-Backed Decision Trees (NBDTs) as a model that harmonizes the interpretability of decision trees with the predictive power of deep learning, specifically targeting applications in machine learning that necessitate both accuracy and justifiability, such as in finance and medicine. The paper addresses the traditional trade-off between accuracy and interpretability by proposing a novel architecture that integrates decision tree-based interpretability into the standard neural network framework.

Methodology and Core Contributions

NBDTs adapt a neural network by substituting the network's final linear layer with a differentiable sequence of decision rules, or a decision tree. Each node in this tree is associated with neural network-derived weights, thus enabling natural path-based decision making which retains high-level class concepts. This approach cleverly ensures that the final prediction process maintains transparency and is inspectable, thereby enhancing human trust and aiding in debugging.

Key contributions of the paper include:

1. Tree Supervision Loss: The authors introduce a tree supervision loss that optimizes the model for accuracy without sacrificing interpretability. This loss improves the original neural network’s performance by up to 2%.
2. Induced Hierarchies: NBDTs create alternative hierarchies using induced hierarchies built from pre-trained neural network weights. These hierarchies outperform conventional data-driven or pre-existing hierarchies, such as those based on WordNet, in terms of accuracy.
3. Generalization and Interpretability: The method allows NBDTs to outperform modern neural networks like WideResNet and EfficientNet on various image classification benchmarks, such as CIFAR-10, CIFAR-100, TinyImageNet, and ImageNet. Notably, they also demonstrate better generalization to unseen classes by up to 16%.

Empirical Evaluation and Results

The experimental section showcases the NBDT’s ability to achieve competitive accuracy levels on established benchmarks. On smaller datasets such as CIFAR-10 and CIFAR-100, NBDTs performed on par with or surpassed traditional baselines. On larger datasets like ImageNet, they significantly outperformed other decision-tree-based models, enhancing state-of-the-art performance with an accuracy increase of 15%. These results validate the efficiency of the tree supervision loss and the informed design of the induced hierarchies.

Implications and Future Directions

The implications of NBDTs are multifaceted:

• Practical Applicability: The dual advantage of accuracy and interpretability positions NBDTs as viable candidates for deployment in sensitive domains where explanation of model decisions is crucial.
• Human Machine Interaction: The enhanced interpretability also means that end-users can gain a better understanding of the model's decision pathways, potentially leading to increased trust in machine-generated predictions.

Looking forward, the integration of NBDTs in dynamic task environments poses intriguing challenges. Enhancements to the current methodology could explore deeper architectural modifications or adaptive learning techniques that react in real-time to data changes, further promoting the applicability of interpretable models in AI.

In conclusion, Neural-Backed Decision Trees effectively leverage the interpretability of decision trees and the accuracy of neural networks, marking a significant stride in developing transparent AI systems. The research opens avenues for further exploration into hybrid model constructions, accentuating the need for balanced models that can be both high-performing and interpretable across varying applications.