Embrace Uncertainty in Machine Learning Models to Maximize Business Value - Covail

'All models are wrong, but some are useful' As this famous quote by George Box (known as the Box Theorem) shows, no model is ever going to be 100% accurate. If one is, run for the hills! Rather, models should be evaluated by their impact on the bottom line, or how useful they are to the business. In this blog post, we will explore a way in which models can be more useful, by embracing and leveraging uncertainty to maximize business results. Much of the time, business users want a single number to represent the'goodness' of a model, but machine learning models can tell us so much more than just a single number (like accuracy).