Аннотация
Applying black-box ML models in high-stakes fields like criminology, healthcare and realtime operating systems might create issues because of poor interpretability and complexity.Also, model building methods that include interpretability is now one of the growing research topics due to the absence of interpretability metrics that are both model-agnostic and quantitative.This paper introduces model selection methods with trade off between interpretability and accuracy of a model.Our results show 97% improvement in interpretability with 2.5% drop in accuracy in AutoMPG dataset using MLP model (65% improvement in interpretability with 1.5% drop in accuracy in MNIST dataset).