# bootstrap aggregating

> ensemble method within machine learning

**Wikidata**: [Q799897](https://www.wikidata.org/wiki/Q799897)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Bootstrap_aggregating)  
**Source**: https://4ort.xyz/entity/bootstrap-aggregating

## Summary
Bootstrap aggregating, or bagging, is an ensemble machine learning method that improves model performance by training multiple instances of the same algorithm on different subsets of data and then combining their predictions. Invented by Leo Breiman, it reduces variance and overfitting, making it a key technique in predictive modeling.

## Key Facts
- **Ensemble method**: Combines multiple models to enhance accuracy and robustness.
- **Inventor**: Developed by Leo Breiman.
- **Subsets**: Uses random sampling with replacement (bootstrap) to create diverse training datasets.
- **Reduces overfitting**: By averaging predictions, it mitigates the risk of overfitting to noise.
- **Part of**: Ensemble learning, a broader category of techniques.
- **Aliases**: Bagging, Agregacion de bootstrap, 自助聚合.
- **Wikipedia presence**: Available in 10 languages (ca, de, en, es, fa, fr, id, it, ja, ko).
- **Wikidata ID**: Q29013802 (discontinued Microsoft Academic ID: 162040801).

## FAQs
### Q: What is the difference between bagging and boosting?
A: Bagging trains multiple models independently on different data subsets and combines their predictions (e.g., via averaging), while boosting trains models sequentially, with each new model correcting errors from the previous one.

### Q: Who invented bagging?
A: Bagging was invented by Leo Breiman, a statistician and computer scientist, as part of his work on ensemble methods in the 1990s.

### Q: How does bagging improve model performance?
A: By training models on random subsets of data and averaging their predictions, bagging reduces variance and overfitting, leading to more stable and accurate predictions.

### Q: What is the "bootstrap" in bootstrap aggregating?
A: The bootstrap refers to random sampling with replacement, a technique used to create diverse training datasets for each model in the ensemble.

### Q: Is bagging only used for decision trees?
A: No, bagging can be applied to any machine learning algorithm, though it is most commonly used with decision trees to create Random Forests.

## Why It Matters
Bootstrap aggregating is significant in machine learning because it addresses a fundamental challenge: reducing overfitting while improving predictive accuracy. By leveraging the wisdom of crowds—combining multiple models—bagging provides a robust framework for handling complex datasets. It is particularly valuable in scenarios where data is noisy or limited, ensuring models generalize well. As a foundational ensemble technique, bagging has influenced the development of more advanced methods like Random Forests and Gradient Boosting, making it a cornerstone of modern predictive modeling.

## Notable For
- **Reduces variance**: Effectively lowers prediction error by averaging multiple models.
- **Versatile**: Applicable to any base model, though most commonly used with decision trees.
- **Foundational**: Inspired later ensemble methods like boosting and stacking.
- **Named in Wikidata**: Recognized as an algorithm and metaheuristic.
- **Multilingual documentation**: Documented in 10 Wikipedia languages, indicating broad adoption.

## Body
### Origins and Invention
Bootstrap aggregating was developed by Leo Breiman, a pioneer in ensemble learning. The method was formalized in the 1990s as a way to improve the stability and accuracy of machine learning models.

### Mechanism
Bagging works by:
1. Creating multiple bootstrap samples (random subsets of the training data with replacement).
2. Training a separate model on each sample.
3. Combining predictions (e.g., via voting or averaging) to produce a final output.

### Applications
Bagging is widely used in:
- **Random Forests**: An extension of bagging that combines it with feature randomness.
- **Regression tasks**: Reducing variance in continuous predictions.
- **Classification tasks**: Improving robustness in categorical predictions.

### Relationships
- **Ensemble learning**: Bagging is a subset of ensemble methods that focus on reducing variance.
- **Metaheuristic**: Classified as a higher-level procedure for heuristic optimization.

### Documentation and Recognition
- **Wikipedia**: Available in multiple languages, reflecting its global relevance.
- **Wikidata**: Linked to BabelNet and Freebase, indicating structured knowledge integration.
- **Academic recognition**: Cited in Microsoft Academic (discontinued) and referenced in statistical literature.

### Limitations
While effective, bagging may not always outperform boosting or other advanced techniques, especially in high-dimensional or imbalanced datasets. However, its simplicity and versatility make it a preferred choice for many practitioners.

## References

1. Bagging predictors
2. BabelNet
3. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)