Interpretable machine learning: definitions, methods, and applications

by   W. James Murdoch, et al.

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.


Interpretability of machine learning based prediction models in healthcare

There is a need of ensuring machine learning models that are interpretab...

Impact of Accuracy on Model Interpretations

Model interpretations are often used in practice to extract real world i...

Interpretation of Time-Series Deep Models: A Survey

Deep learning models developed for time-series associated tasks have bec...

Pitfalls to Avoid when Interpreting Machine Learning Models

Modern requirements for machine learning (ML) models include both high p...

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations

Non-linear machine learning models often trade off a great predictive pe...

Truthful Meta-Explanations for Local Interpretability of Machine Learning Models

Automated Machine Learning-based systems' integration into a wide range ...

Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations

While the need for interpretable machine learning has been established, ...

Please sign up or login with your details

Forgot password? Click here to reset