Which term describes the process of applying a chosen predictive model to score data?

The process of applying a chosen predictive model to score data is referred to as implementation. This term encapsulates the phase where the trained model is put to use on new or unseen data to generate predictions or scores. Implementation is crucial as it allows for the practical application of insights gained during the modeling phase, translating theoretical model outputs into actionable results in a real-world context.

Training is the phase where the model learns from historical data, while validation refers to the assessment of the model’s performance on a separate dataset to ensure it generalizes well. The term "model" by itself simply refers to the mathematical or computational construct that represents the relationships learned from data, but it does not indicate the process of applying that model to make predictions. Therefore, implementation is the correct term that describes the complete cycle - from creation to practical application of predictive models.