bootstrapaggregating

Bootstrap aggregating, often shortened to Bagging, is a general-purpose ensemble learning method. It is a technique used to improve the stability and accuracy of machine learning algorithms. Bagging belongs to the family of ensemble methods, which combine multiple machine learning models to produce an optimal predictive model.

The core idea behind Bagging is to reduce variance, a common problem in machine learning that leads to overfitting. Overfitting occurs when a model learns the training data too well, including its noise, and performs poorly on unseen data. Bagging aims to mitigate this by creating multiple independent models and averaging their predictions.

The process begins with bootstrap sampling. This involves creating multiple subsets of the original training dataset by randomly sampling with replacement. Each subset is roughly the same size as the original dataset, but some data points may appear multiple times, while others may be omitted.

Each of these bootstrap samples is then used to train an independent instance of a chosen base learning algorithm. For example, if the base learner is a decision tree, Bagging would create an ensemble of decision trees.

Finally, for a classification task, the predictions from all the individual models are aggregated, typically by taking a majority vote. For a regression task, the predictions are averaged. This aggregation process helps to smooth out the predictions and reduce the impact of outliers or individual model errors.

Bagging is particularly effective with unstable learners, meaning algorithms whose output can change dramatically with small changes in the training data. Decision trees are a prime example of such unstable learners. By using Bagging, the overall variance of the model is reduced, leading to improved generalization performance.