Glossary · Phase 3 · Machine learning
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Trying every combination from a list of settings, scoring each with cross-validation, and keeping the best.
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Every row gets to be test data once, so one lucky split can't fool you.
Learn it properly
Cross-validation · Reuse limited data for more reliable estimates. About 30 minutes.
Other terms from the same lesson
- cross-validation: Scoring a model several times on different held-back slices of the data, for a steadier estimate.
- fold: One of the equal slices that cross-validation cuts the data into. Each fold takes a turn as the held-back data.
- hyperparameter: A setting you choose before training, such as tree depth or learning rate, rather than one the model learns.
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