Bagging Predictors

Summary

Introduces bootstrap aggregating: fitting a model to many bootstrap resamples of the training data and averaging the predictions, which reduces variance without increasing bias.

Why it matters

It gave a general, model-agnostic recipe for converting an unstable predictor into a stable one, and identified instability as the precise property that determines whether averaging helps. Random forests and, more broadly, the entire ensemble tradition follow directly from it.