Malte Tichy
After pursuing his PhD and postdoc research in theoretical quantum physics, Malte joined Blue Yonder as a Data Scientist in 2015. Since then, he has led numerous external and internal projects, which all involved programming python, creating, working with and evaluating probabilistic predictions, and communicating the achieved results.
Sessions
Meaningful probabilistic models do not only produce a “best guess” for the target, but also convey their uncertainty, i.e., a belief in how the target is distributed around the predicted estimate. Business evaluation metrics such as mean absolute error, a priori, neglect that unavoidable uncertainty. This talk discusses why and how to account for uncertainty when evaluating models using traditional business metrics, using python standard tooling. The resulting uncertainty-aware model rating satisfies the requirements of statisticians because it accounts for the probabilistic process that generates the target. It should please practitioners because it is based on established business metrics. It appeases executives because it allows concrete quantitative goals and non-defensive judgements.