cross-validation: meaning, definition, pronunciation and examples
C2Formal / Technical / Academic
Quick answer
What does “cross-validation” mean?
A statistical method where a dataset is partitioned to assess how the results of an analysis will generalize to an independent data set.
Audio
Pronunciation
Definition
Meaning and Definition
A statistical method where a dataset is partitioned to assess how the results of an analysis will generalize to an independent data set.
A model validation technique used to estimate the skill of machine learning models. It is also used more broadly to describe the process of checking or verifying something against multiple, independent sources or methods.
Dialectal Variation
British vs American Usage
Differences
No significant difference in meaning or usage. Spelling conventions for the component words 'cross' and 'validation' are identical.
Connotations
Identical technical connotations in both varieties.
Frequency
Equally frequent in technical/academic contexts in both varieties.
Grammar
How to Use “cross-validation” in a Sentence
undergo cross-validationsubject N to cross-validationN is based on cross-validationvalidate by cross-validationVocabulary
Collocations
Examples
Examples of “cross-validation” in a Sentence
verb
British English
- The researcher decided to cross-validate the findings using a bootstrapping method.
- All predictive models must be cross-validated before deployment.
American English
- We need to cross-validate our algorithm against the new clinical data.
- The team cross-validated the model's parameters to ensure robustness.
adjective
British English
- The cross-validation results were appended to the report.
- A cross-validation framework was essential for the experiment.
American English
- She presented the cross-validation metrics in a detailed table.
- The cross-validation procedure followed standard k-fold protocol.
Usage
Meaning in Context
Business
Rare, except in data-driven roles (e.g., 'The marketing model's accuracy was confirmed by cross-validation.')
Academic
Very common in statistics, data science, machine learning, and quantitative social sciences papers.
Everyday
Extremely rare. Would be misunderstood or require explanation.
Technical
The primary context. Central to discussions of predictive model performance and avoiding overfitting.
Vocabulary
Synonyms of “cross-validation”
Strong
Neutral
Weak
Vocabulary
Antonyms of “cross-validation”
Watch out
Common Mistakes When Using “cross-validation”
- Using it as a verb without a hyphen (e.g., 'We cross validated' should be 'We cross-validated').
- Using it in non-technical contexts where 'verification' or 'cross-checking' would be clearer.
- Misspelling as one word: 'crossvalidation'.
FAQ
Frequently Asked Questions
While it is a cornerstone of machine learning and modern statistics, the concept can be applied in other fields where model generalisability is tested, such as certain psychological or econometric modelling.
It refers to splitting the dataset into 'k' number of equal-sized subgroups or 'folds'. The model is trained on k-1 folds and tested on the remaining fold. This process is repeated k times, with each fold used exactly once as the test set.
No. It provides an estimate of a model's performance on unseen data drawn from the same distribution as the training data. It cannot guarantee performance on data from a completely different source or context.
Validation typically involves a single, held-out test set. Cross-validation systematically repeats the validation process multiple times on different data partitions, providing a more robust and less variable performance estimate.
A statistical method where a dataset is partitioned to assess how the results of an analysis will generalize to an independent data set.
Cross-validation is usually formal / technical / academic in register.
Cross-validation: in British English it is pronounced /ˌkrɒs ˌvæl.ɪˈdeɪ.ʃən/, and in American English it is pronounced /ˌkrɔːs ˌvæl.əˈdeɪ.ʃən/. Tap the audio buttons above to hear it.
Learning
Memory Aids
Mnemonic
Think of CROSS-ing two pieces of VALIDATION: you train your model on one part of the data and validate it on the other, then cross over and repeat.
Conceptual Metaphor
VALIDATION AS TESTING ON UNSEEN TERRITORY (the model is 'trained' in one 'country' (dataset) and must prove it can work in a new, unseen 'country').
Practice
Quiz
What is the primary purpose of cross-validation?