MonoNet: Towards Interpretable Models by Learning Monotonic Features

Understand

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.