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Glossary term
How much a model's predictions change when it is trained on different data.
In machine learning, variance describes how much a model's predictions would change if it were trained on a different sample of data. A high-variance model is very sensitive to the specifics of its training set.
It is the partner of bias in the bias-variance trade-off: simple models tend to have high bias and low variance, flexible ones the reverse.
Understanding this trade-off explains why both under- and over-complex models fail.