  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.
