MonoNet: Towards Interpretable Models by Learning Monotonic Features

TL;DR

MonoNet introduces a method to enhance interpretability of neural networks by enforcing monotonic relationships between features and outputs, allowing independent reasoning about feature effects, validated on benchmark datasets.

Contribution

This paper proposes a novel deep learning architecture that incorporates monotonicity constraints to improve interpretability, addressing the challenge of feature interactions in complex models.

Findings

Monotonic constraints improve interpretability of neural networks.

The model performs competitively on benchmark datasets.

Enhanced feature effect reasoning is achieved through structural constraints.

Abstract

Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data-driven models to make high-stakes decisions, e.g. in healthcare. While recent years have seen an increasing interest in interpretable machine learning research, this field is currently lacking an agreed-upon definition of interpretability, and some researchers have called for a more active conversation towards a rigorous approach to interpretability. Joining this conversation, we claim in this paper that the difficulty of interpreting a complex model stems from the existing interactions among features. We argue that by enforcing monotonicity between features and outputs, we are able to reason about the effect of a single feature on an output independently from other features, and consequently better understand the model. We show how to structurally introduce this constraint in deep learning models by adding new simple layers. We validate our model on benchmark datasets, and compare our results with previously proposed interpretable models.