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The importance of domain knowledge in enhancing model performance and makingreliable predictions in the real-world is critical. This has led to anincreased focus on specific model properties for interpretability. We focus onincorporating monotonic trends, and propose a novel gradient-based point-wiseloss function for enforcing partial monotonicity with deep neural networks.While recent developments have relied on structural changes to the model, ourapproach aims at enhancing the learning process. Our model-agnostic point-wiseloss function acts as a plug-in to the standard loss and penalizesnon-monotonic gradients. We demonstrate that the point-wise loss producescomparable (and sometimes better) results on both AUC and monotonicity measure,as opposed to state-of-the-art deep lattice networks that guaranteemonotonicity. Moreover, it is able to learn differentiated individual trendsand produces smoother conditional curves which are important for personalizeddecisions, while preserving the flexibility of deep networks.

How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility?