How to improve your machine learning models by explaining predictions with LIME

Even though we like the idea that we never make mistakes, every software can contain bugs. Assuming that we may use Machine Learning models to make decisions in the real world, a bug in our code can be very dangerous. Relying only on the prediction accuracy might not be a good idea, because if we get a good accuracy score we might not even consider that there is a bug in our data pipeline. Most Machine Learning algorithms are black boxes, but LIME has a bold value proposition: explain the results of any predictive model. The tool can explain models trained with text, categorical, or continuous data.