The last two days, I was in London for the M-cubed conference.
Here are the slides from my talk about Explaining complex machine learning models with LIME:
Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria.
