MonoNet: Towards Interpretable Models by Learning Monotonic Features
This work addresses the need for interpretability in high-stakes decision-making, such as healthcare, by introducing a method to make models more understandable, though it is incremental in nature.
The paper tackles the problem of interpreting complex machine learning models by proposing to enforce monotonicity between features and outputs, enabling independent reasoning about individual feature effects. They validate their approach on benchmark datasets and compare it with existing interpretable models.
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.