What is variance in machine learning and why does it cause overfitting?

Variance is the sensitivity of a model to small fluctuations in training data.

Variance measures how much a model’s predictions change when trained on different subsets of data. High variance models learn noise along with patterns, leading to overfitting. Complex models like deep decision trees or high-degree polynomial regression typically have high variance. Reducing variance often involves regularization, pruning, or using ensembles.