Bagging (Bootstrap Aggregating)

Bagging, an acronym for bootstrap aggregating, is an ensemble machine learning algorithm. The general idea of bagging is to create several subsets of data from the original dataset, with replacement, and train a model for each subset. The final prediction is calculated by averaging the predictions (in case of regression problem) or by voting (in case of a classification problem) from each model.

How Bagging works

Bagging works by following these steps:

1. Multiple subsets are created from the original dataset, using bootstrap (random sampling with replacement).
2. A base model is created on each of these subsets. The model can be any machine learning algorithms like decision trees, logistic regression, neural networks, etc.
3. Each model is then trained independently of the others, allowing them to learn from the different sampling of the dataset.
4. For a prediction, all the individual models predict the output and then the final prediction is decided either by taking the majority vote (in the case of classification) or by averaging the predictions (in the case of regression).

This approach helps to reduce the variance and avoid overfitting. One of the most common uses of bagging is in the Random Forest algorithm, in which a collection of decision trees are bagged to get the final outcome.