Hold-out validation
Hold-out validation is an evaluation procedure that partitions data once into separate subsets for model fitting and assessment.
Deep Learning with R, Third Edition
Hold-out validation is an evaluation procedure that reserves a subset of available data for assessment without using it to fit the model.
- How it is used
- Evaluates generalization during model development by reserving validation data for performance measurement and decision-making.
- How deep it goes
- Explains the split, training loop, test separation, leakage risk, and small-data limitation, then points to K-fold alternatives.
- Examples
- Uses indexed data partitioning, model fitting, validation scoring, final retraining, and test evaluation.
- What you can do after
- Apply it by splitting data, tuning against validation metrics, retraining after decisions, and reserving the test set for final evaluation.
Read how Deep Learning with R, Third Edition explains hold-out validation →
Deep Learning with Python, Third Edition
An evaluation protocol that splits data once into separate subsets for training, validation, and/or testing.
- How it is used
- Uses a single data split to estimate validation performance while preserving a separate test set for final generalization measurement.
- How deep it goes
- Explains the split, training, evaluation, retraining, and small-data caveat, then defers repeated partitioning to K-fold methods.
- Examples
- Demonstrates shuffled array slicing, model fitting, validation evaluation, and final retraining with combined training and validation data.
- What you can do after
- Apply hold-out validation by splitting data, fitting a model, checking validation performance, and reserving test data for final evaluation.
Read how Deep Learning with Python, Third Edition explains hold-out validation →
Related concepts
- cross-validationalternative to · Cross-validation is a resampling technique that evaluates a model by repeatedly fitting it on some portions of available data and assessing it on held-out portions.
- evaluation datasetuses · An evaluation dataset is a collection of examples or test cases, often with expected or judged outcomes, designated to assess a model or system against defined criteria.
- training datasetimplements · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.