Glossary · Phase 3 · Machine learning
Validation set
Held-back examples used to compare and tune models while you work.
Try it now
Each set answers one question. Mixing their jobs is how inflated scores happen.
Learn it properly
Train, validation and test sets · Hold data back so you can trust your results. About 25 minutes.
Other terms from the same lesson
- data leakage: When information from the test data sneaks into training, so the model looks better than it really is on new data.
- stratified split: Splitting data so each part keeps the same mix of classes as the whole, for example the same share of churners in train and test.
- test set: Examples kept aside until the very end to give an honest score on unseen data.
- training set: The examples a model learns from.
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