Glossary · Phase 3 · Machine learning
Bias-variance tradeoff
Simple models miss in a steady way (bias); flexible models swing with every change in the data (variance). The best model balances the two.
Try it now
Twelve refits on fresh samples. At degree 1 they agree and all miss. At degree 12 they disagree wildly.
Learn it properly
Bias and variance · The two ways a model can be wrong. About 30 minutes.
Other terms from the same lesson
- double descent: When a model is made far bigger than the data seems to justify, its test error can fall again after first rising. It's one reason huge neural networks still generalize.
- learning curve: A chart of training and validation scores as the training set grows. The gap between the lines shows whether more data would help.
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