AI Validation

Model Checking is a formal verification technique used to ensure that systems meet specified properties.

Monte Carlo Cross-Validation is a statistical method for estimating the performance of machine learning models using random sampling.

Out-of-Sample Evaluation assesses an AI model's performance on unseen data to gauge its generalization ability.

Parameter validity refers to the accuracy and appropriateness of parameters used in AI models.

A Verifier Model is a system that checks the accuracy of another model's outputs.