Short Talk - Demystifying machine learning using lime
Machine learning models are often dismissed on the grounds of lack of interpretability. There is a popular story about modern algorithms that goes as follows: Simple linear statistical models such as logistic regression yield to interpretable models, on the other hand, advanced models such as random forest or deep neural networks are black boxes. Meaning that it is nearly impossible to understand how a model is making a prediction.
In this talk I present a case study of the LIME model. LIME stands for Local Interpretable Model-agnostic Explanations, and its objective is to explain the result from any classifier so that a human can understand individual predictions.