Explaining Machine Learning Predictions and Building Trust with LIME

A technique to explain how black-box machine learning classifiers make predictions

Catherine Yeo
Fair Bytes
Published in
6 min readAug 13, 2020


Photo by Joshua Hoehne on Unsplash

It’s needless to say: machine learning is powerful.

At the most basic level, machine learning algorithms can be used to classify things. Given a collection of cute animal pictures, a classifier can separate the pictures into buckets of ‘dog’ and ‘not a dog’. Given data about customer restaurant preferences, a classifier can predict what restaurant a user goes to next.

However, the role of humans is overlooked in the technology. It does not matter how powerful a machine learning model is if one does not use it. With so little explanation or reasoning as to how these algorithms made their predictions, if users do not trust a model or a prediction, they will not use it.

“If the users do not trust a model or a prediction, they will not use it.”

As machine learning becomes deployed in even more domains, such as medical diagnosis and recidivism, the decisions these models make can have incredible consequences. Thus, it is of utmost importance to understand and explain how their predictions came to be, which then builds trust.

In their paper “‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier”, Ribeiro, Singh, and Guestrin present a new technique to do so: LIME (Local Interpretable Model-agnostic Explanations). This post will summarize their findings and introduce LIME.

One line summary

LIME is a new technique that explains predictions of any machine learning classifier and has been shown to increase human trust and understanding.

Explaining predictions

Figure 1 from paper

Why is explaining predictions useful?

Let’s look at the example use case of medical diagnosis. Given the patient’s symptoms and measurements, a doctor must make their best judgment as to what the…



Catherine Yeo
Fair Bytes

Harvard | Book Author | AI/ML writing in @fairbytes @towardsdatascience | More at catherinehyeo.com