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Understanding validation samples


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.

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