What term describes the process of checking the accuracy of a predictive model?

The term that best describes the process of checking the accuracy of a predictive model is validation. Validation involves assessing the performance of a model using a separate dataset that was not used during the training phase. This process is crucial because it helps determine how well the model can generalize to unseen data, thereby ensuring its reliability and effectiveness.

In predictive analytics, validation can involve various techniques, such as cross-validation or the use of a validation dataset. The goal is to verify that the model has not merely memorized the training data, which could lead to overfitting, but rather that it has learned to make accurate predictions based on patterns in the data.

While testing, evaluation, and calibration are related concepts, they do not specifically encompass the entire process of checking the predictive accuracy of a model in the same way that validation does. Testing often refers to running the model on a specific dataset, evaluation can encompass a broader assessment of model performance metrics, and calibration involves adjusting the model's predictions to improve accuracy but does not focus solely on validating the model's performance against a standard. Thus, validation is the precise term for this critical assessment stage in the predictive modeling lifecycle.