Bias-variance tradeoff

Also written: bias-variance trade-off

Simple models miss in a steady way (bias); flexible models swing with every change in the data (variance). The best model balances the two.

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.