Cross Validation
Statistical method for assessing how well an AI model will generalize to new data by testing on multiple data subsets.
Definition
Cross validation systematically divides datasets into training and testing portions, repeatedly training models on different subsets and evaluating performance on held-out data to estimate real-world accuracy.
Techniques like k-fold cross validation and stratified sampling ensure robust model evaluation while maximizing the use of available training data and identifying potential overfitting issues.
Why It Matters
Cross validation helps businesses avoid deploying AI models that perform well in testing but fail in production, preventing costly implementation failures and maintaining customer trust.
This validation approach provides confidence intervals for model performance estimates, enabling data-driven decisions about model deployment and helping justify AI investments to stakeholders.
Examples in Practice
Medical device companies use cross validation to ensure diagnostic AI systems maintain consistent accuracy across different patient populations before seeking regulatory approval.
E-commerce platforms employ cross validation when testing new recommendation algorithms, ensuring performance improvements are statistically significant before rolling out changes to millions of users.
Insurance companies apply cross validation to risk assessment models, validating that pricing algorithms perform consistently across different geographic regions and demographic groups to maintain profitability.