Scoring

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In machine learning, scoring is the process of applying an algorithmic model built from a historical dataset to a new dataset in order to uncover practical insights that will help solve a business problem.

Model development is generally a two-stage process.

  • The first stage is training and validation, during which you apply algorithms to data for which you know the outcomes to uncover patterns between its features and the target variable.
  • The second stage is scoring, in which you apply the trained model to a new dataset. Then, it returns outcomes in the form of probability scores for classification problems and estimated averages for regression problems. Finally, you deploy the trained model into a production application or use the insights it uncovers to improve business processes.