What refers to determining how often a model should score new data?

The concept of determining how often a model should score new data is known as Frequency of Scoring. This refers to the schedule or intervals at which a model evaluates new incoming data to generate predictions. The frequency is critical as it can impact the relevance and accuracy of the predictions. For instance, some applications may require real-time scoring, while others can function with daily, weekly, or even monthly scoring, depending on the dynamics of the data being analyzed and the business needs.

In contrast, model validation relates to assessing the performance of a model based on historical data to ensure its predictions are reliable. Data normalization involves processing input data to ensure it fits within a certain range or distribution, making the data easier to work with for modeling. Feature selection focuses on identifying the most significant predictors in a dataset to improve the model’s performance and interpretability. Each of these areas plays a distinct role in the development and deployment of a data science solution, but none pertains directly to the periodic evaluation of new data by a model, which is what Frequency of Scoring specifically addresses.