Predictive Multiplicity in Classification

Understand

Prediction problems often admit competing models that perform almost equally well.

• This effect challenges key assumptions in machine learning when competing models assign conflicting predictions.
• In this paper, we define predictive multiplicity as the ability of a prediction problem to admit competing models with conflicting predictions.
• We introduce formal measures to evaluate the severity of predictive multiplicity and develop integer programming tools to compute them exactly for linear classification problems.