Definition: A technique for assessing the performance of a model by training and evaluating it on different subsets of the data.
Better definition: When your computer gets graded on multiple pop quizzes to make sure it's really learned its stuff.
Where does this fit in the AI Landscape?
Cross-validation is an essential technique for model evaluation and selection in AI and machine learning. It helps researchers avoid overfitting and ensure that their models perform well on unseen data. Cross-validation is widely used across industries and forms the basis of reliable and effective AI systems.
What are the real world impacts of this?
Cross-Validation techniques ensure that machine learning models are reliable and perform well on unseen data. This leads to better, more reliable predictions in applications like health diagnosis, financial forecasting, and personalized recommendations. For developers, cross-validation is a key technique for model evaluation and selection.
What could go wrong in the real world with this?
A model is cross-validated for its ability to predict TV show popularity but becomes convinced that a long-lost, obscure sitcom from the 1960s will make a sudden and triumphant return to the top of the ratings.
A technique for assessing how well the model will generalize to new data. It can be used during model training to validate the model's performance on unseen data.