MLI Overview

Driverless AI provides robust interpretability of machine learning models to explain modeling results in a human-readable format. In the Machine Learning Interpetability (MLI) view, Driverless AI employs a host of different techniques and methodologies for interpreting and explaining the results of its models. A number of charts are generated automatically (depending on experiment type), including K-LIME, Shapley, Variable Importance, Decision Tree Surrogate, Partial Dependence, Individual Conditional Expectation, Sensitivity Analysis, NLP Tokens, NLP LOCO, and more. Additionally, you can download a CSV of LIME and Shapley reasons codes from this view.

This chapter describes Machine Learning Interpretability (MLI) in Driverless AI for both regular and time-series experiments. Refer to the following sections for more information:

Additional Resources


  • This release deprecates experiments run in 1.7.0 and earlier. MLI will not be available for experiments from versions <= 1.7.0.

  • MLI is not supported for multiclass Time Series experiments.

  • MLI does not require an Internet connection to run on current models.