Motivated by the prevalence of prediction problems in the economy, we study mar- kets in which firms sell models to a consumer to help improve their prediction. Firms

decide whether to enter, choose models to train on their data, and set prices. The con- sumer can purchase multiple models and use a weighted average of the models bought.

Market outcomes can be expressed in terms of the bias-variance decompositions of the models that firms sell. We give conditions when symmetric firms will choose different modeling techniques, e.g., each using only a subset of available covariates. We also show firms can choose inefficiently biased models or inefficiently costly models to deter entry by competitors.