What is variance in machine learning?

Updated May 15, 2026

Short answer

Variance is the model’s sensitivity to small changes in training data, causing unstable predictions.

Deep explanation

Variance measures how much model predictions change when trained on different datasets. High variance occurs in overly complex models that memorize training data instead of learning general patterns. This leads to overfitting and poor generalization to unseen data.

Real-world example

A deep decision tree perfectly classifies training data but performs poorly on new customers.

Common mistakes

Thinking variance is related to statistical variance only.

Follow-up questions

How do you reduce variance?
Is high variance always due to complex models?

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