AI engineering glossary · ML fundamentals
What is holdout method?
Holdout method (also called Train-test split): The simplest way to estimate performance on new data: split the dataset once into a training part and a held-out part, train on the first and evaluate on the second. It is fast, but the estimate depends on which rows happened to land in each part, so it can be noisy on small datasets.
Holdout method: an example
An 80/20 random split of 5,000 records trains on 4,000 and evaluates on 1,000.
Related terms
More ML fundamentals terms
- Hyperparameter
- Hyperparameter tuning
- Hypothesis space
- Independent and identically distributed
- Inductive bias
- Inference
- Inter-annotator agreement
- Irreducible error
Where you learn it
Holdout method is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.