Cross-Validation
/ˌkrɔːs ˌvælɪˈdeɪʃən/cross·val·i·da·tionnoun
Definition
1.[in machine learning] a technique used to estimate the performance of a model on unseen data by partitioning the dataset into multiple subsets.
We used k-fold Cross-Validation to ensure our model's performance was robust across different data splits.
Formal statement
Etymology
From cross- 'crosswise, reciprocally' plus validation, Latin validus 'strong'. The prefix names the reciprocal arrangement in which each subset serves in turn as the held-out set. Resampling of this kind appears in the 1930s; the method was formalised and named by Stone and by Geisser, independently, in 1974.
Synonyms
- k-fold validationsense 1 · Near
See also
References
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning, 2nd ed., ch. 5.Springer.