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cross validation
This term is a technical staple of data science and statistics, specifically used to describe the process of assessing how a result will generalize to an independent data set. It carries a connotation of rigor and reliability, as it is the primary defense against overfitting, where a model learns noise rather than the underlying pattern.
In professional contexts, it is often used as a shorthand for the specific k-fold method, though other variations like leave-one-out exist. It is almost exclusively used in academic, technical, or corporate data-driven environments and would be out of place in general conversation.
Meanings
A technique in machine learning and statistics used to evaluate the performance of a predictive model by partitioning a dataset into subsets, training the model on one subset, and validating it on the remaining subset to ensure the model generalizes well to unseen data.
The researcher used k-fold cross validation to ensure the model was not overfitting the training data.