Description
Out-of-sample testing is fundamental to validating AI model performance in real-world conditions. While in-sample testing can provide optimistic estimates of model capabilities, only rigorous out-of-sample testing can reveal how models will truly perform when deployed in production environments.
The challenge lies in designing and implementing testing frameworks that effectively simulate real-world conditions while maintaining statistical validity. Here is an approach to out-of-sample testing, helping organizations build confidence in their AI models’ generalization capabilities.
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