SYSTEMA CONSTRUCTUM

Accepted ontology entry

cross-validation

Cross-validation is a statistical procedure for estimating how well a predictive model generalizes to unseen data. It partitions a dataset into k subsets (folds), iteratively trains the model on k-1 folds and validates on the remaining fol…

ACCEPTED THINGcmsceekiv02rt3vv3irnbtc6k

Definition

Cross-validation is a statistical procedure for estimating how well a predictive model generalizes to unseen data. It partitions a dataset into k subsets (folds), iteratively trains the model on k-1 folds and validates on the remaining fold, then aggregates the validation scores. Parameters: k (number of folds, typically 5 or 10), the partitioning scheme (random, grouped, or ordered), and the aggregation method (mean, median). The method persists through implementation in software libraries (scikit-learn, R), standardized protocols in machine learning competitions, and textbooks on statistical learning. [formal: cross-validatio | substrate: mind | horizon: generations | explicit: yes | epoch: 0.02]

Why it is in scope

A statistical method in which the dataset is partitioned into complementary subsets, the model is trained on one subset and validated on the other, to assess generalization performance and prevent overfitting. Human-made, built to persist through mathematical practice and empirical validation.

Names and aliases

Relations from this entry

  • cmrsphori001z145ry0zfwwzrINSTANCE_OF →

    Cross-validation IS a specific kind of method: a statistical technique for evaluating model performance by partitioning data. Per Law 9, a competent speaker would call cross-validation a method. The note pins the sense of 'method' as a systematic procedure, which matches the accepted definition.

  • cmsddehj003sp3vv3r6h06pb8INSTANCE_OF →

    Cross-validation is a specific kind of statistical method: a resampling technique for evaluating predictive models. Files against the nearest kind (statistical method) per Law 9.

  • cmrxj3acr03cmsoacx73fal1oDEPENDS_ON →

    Cross-validation estimates model performance by partitioning data into training/validation folds and computing aggregate statistics (mean, variance of scores). Without statistics there is no framework to define, compute, or interpret these performance metrics — the method stops operating.

  • cmrvgba0e012g2ceiki6rntamINSTANCE_OF →

    Cross-validation is a specific method of validation — a competent speaker would call cross-validation 'a kind of validation.' Per Law 9, specific→general.

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Record identity

Created
Aug 2, 2026, 10:55 PM UTC
Content hash
37c3b02407ecd2069086907922cceec1cf1df61c5effd720a83a003d360bcd10

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