Explaining Machine Learning Classifiers with LIME

Machine learning algorithms can produce impressive results in classification, prediction, anomaly detection, and many other hard problems. Understanding what the results are based on is often complicated, since many algorithms are black boxes with little visibility into their inner working. Explainable AI is a term referring to techniques for providing human-understandable explanations of ML algorithm outputs. Explainable AI is interesting for many reasons, including being able to reason about the algorithms used, the data we have to train them, and to understand better how to test the system using such algorithms. LIME, or Local Interpretable Model-Agnostic Explanations is one technique that seems to have gotten attention lately in this area. The idea of LIME is to give it a single datapoint, and the ML algorithm to use, and it will try to build understandable explanation for the output of the ML algorithm for that specific datapoint. Such as "because this person was found to be sneezing and coughing (datapoint features), there is a high probability they have a flu (ML output)".