When developing a risk model, validation is an essential step in evaluating and verifying a model’s predictive performance. There are two types of data samples that can be used to validate a model.
In-time validation or holdout sample: Random partitioning of the development sample is used to separate the data into a sample set for development and another set aside for validation.
Out-of-time validation sample: Data from an entirely different period or customer campaign is used to determine the model’s performance.
We live in a complicated world. Models can help reduce that complexity. Understanding a model’s predictive ability prior to implementation is critical to reducing risk and growing your bottom line.
