Scoring is widely used in machine learning to mean the process of generating new values, given a model and some new input. The generic term “score” is used, rather than “prediction,” because the scoring process can generate so many different types of values:

  • A list of recommended items and a similarity score.
  • Numeric values, for time series models and regression models.
  • A probability value, indicating the likelihood that a new input belongs to some existing category.
  • The name of a category or cluster to which a new item is most similar.
  • A predicted class or outcome, for classification models.

Scoring is also called prediction, and is the process of generating values based on a trained machine learning model, given some new input data. The values or scores that are created can represent predictions of future values, but they might also represent a likely category or outcome. A Scoring model when applied to an individual produces a score representing the probability that the individual belongs to each class. In our customer response example, a scoring model can evaluate each individual customer and produce a score of how likely each customer is to respond to the offer.