What is one of the key goals of model scoring in SAS?

One of the primary goals of model scoring in SAS is to generate predictions for new, unseen data. This process involves applying a developed model to a new dataset that was not part of the original training data, allowing the model to predict outcomes based on the input variables present in this new data. Model scoring is fundamental in practical applications, as it helps organizations make informed decisions based on predictions.

By scoring new data, businesses can leverage insights gained from their models to assess risks, optimize operations, enhance customer targeting, and improve overall performance. This ability to provide actionable predictions is crucial for the effectiveness of predictive analytics in various domains, ranging from marketing to finance.

In contrast, acquiring additional data for training focuses on enhancing the model by feeding it more information, validating models exclusively centers on assessing the performance of existing models rather than applying them, and adjusting existing data for accuracy processes pertains to data cleansing or transformation, which is not the main goal of scoring.