Why do complex models tend to have high variance?

Updated May 15, 2026

Short answer

Complex models learn noise in training data, making them sensitive to small changes, leading to high variance.

Deep explanation

Complex models have many parameters and high flexibility, allowing them to fit training data extremely well. However, this flexibility also makes them sensitive to small variations in the dataset. As a result, different training samples produce significantly different models.

Real-world example

A deep decision tree changes drastically when trained on slightly different customer datasets.

Common mistakes

Confusing complexity with intelligence of model.

Follow-up questions

Does pruning reduce variance?
Is high variance always bad?

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