Finding the Good, Bad, and Ugly Predictions You're Making Daily
Morgan Hansen, director of data science at ALG, a TrueCar company, discusses the importance of visualizing predictions during his presentation at DataScience: Elevate.
Building a machine learning model that minimizes prediction error is a core skill for predictive data scientists. Picking the right cost function is usually easy enough, but understanding how well your model is predicting all the nooks and crannies of a diverse data set can help surface trends and groupings that may have gone unnoticed.
During his talk at DataScience: Elevate, Morgan Hansen discusses how visualizing your predictions — either segmented manually or by algorithmically defined categories — can help you identify segments of your prediction population that may need additional attention, as well as latent variables and potential business risks.