Intermediate · Research
Bagging
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. An ensemble technique where models are trained on random subsets of data sampled with replacement to reduce variance.
Technical Definition
An ensemble technique where models are trained on random subsets of data sampled with replacement to reduce variance.
How it works
Bagging, short for bootstrap aggregating, is an ensemble learning method that improves model stability and accuracy. It involves training multiple models independently on different bootstrap samples (random subsets of the training data with replacement) of the original dataset. The final prediction is typically an aggregation of the predictions from these individual models, like in Random Forests.
Related Concepts
- Bootstrap aggregating — An ensemble technique that trains multiple models independently and averages their predictions to reduce variance, commonly known as bagging.
- Ensemble learning — Combining multiple machine learning models to improve overall prediction accuracy and robustness.
- Random forest — An ensemble machine learning method that builds multiple decision trees to improve prediction accuracy and control overfitting.