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Showing posts from January, 2017

Explaining the decisions of machine learning algorithms

Being both statistician and machine learning practitioner, I have always been interested in combining the predictive power of (black box) machine learning algorithms and the interpretability of statistical models.

I thought the only way to combine predictive power and interpretability is by using methods that are somewhat in the middle between 'easy to understand' and 'flexible enough', like decision trees or the RuleFit algorithm or, additionally, by using techniques like partial dependency plots to understand the influence of single features. Then I read the paper "Why Should I Trust You" Explaining the Predictions of Any Classifier [1], which offers a really decent alternative for explaining decisions made by black boxes.

What is LIME? The authors propose LIME, an algorithm for Local Interpretable Model-agnostic Explanations. LIME can explain why a black box algorithm assigned a specific classification/prediction to one datapoint (image/text/tabular data) b…