What is bootstrap aggregation variance reduction intuition?

Bagging reduces variance by averaging predictions from models trained on different bootstrap samples.

Bootstrap aggregation (bagging) reduces variance by training multiple models on different randomly sampled datasets (with replacement). Each model learns slightly different patterns due to data variation. When predictions are averaged, random fluctuations cancel out, reducing overall variance. This is especially effective for unstable models like decision trees.