Bagging, short for Bootstrap Aggregating, is an ensemble learning technique used in machine learning to improve the stability and accuracy of algorithms, particularly decision trees. Here's a breakdown of how bagging works:
- Multiple Subsets: Bagging creates multiple subsets of the original dataset through bootstrapping (random sampling with replacement), allowing the same instance to appear more than once in a subset.
- Parallel Training: Each subset trains a separate model, typically of the same type, in parallel and independently.
- Aggregating Predictions: The final prediction is an aggregation of all model predictions. For regression, this is usually the average; for classification, it's typically the mode (most frequent prediction).
Advantages of bagging include:
- Reduction in Variance: Averaging multiple models can significantly reduce variance without increasing bias, ideal for high-variance, low-bias models like decision trees.
- Improves Accuracy: Ensemble methods like bagging often result in higher overall accuracy.
- Robustness to Overfitting: Using multiple data subsets helps reduce overfitting risk, especially in noisy datasets.
An example of a bagging algorithm is the Random Forest, which combines the simplicity of decision trees with the power of ensemble learning, resulting in robust and accurate models.