Selfbagging

Selfbagging is a machine learning ensemble technique that involves training multiple models on different bootstrap samples of the training data. Bootstrap sampling is a method of resampling with replacement, meaning that some data points may appear multiple times in a single bootstrap sample, while others may not appear at all. Each of these bootstrap samples is then used to train an independent model.

The core idea behind selfbagging is to reduce variance. By training on different subsets of the data, the individual models are likely to make different errors. When the predictions of these individual models are combined, typically through averaging for regression tasks or majority voting for classification tasks, the common errors tend to cancel out, leading to a more robust and accurate overall prediction. This process is similar to the more widely known bagging (Bootstrap Aggregating) algorithm, with the key distinction being that selfbagging is often applied to a single base learner, effectively creating an ensemble of identical base learners trained on different data subsets.

Selfbagging is particularly effective for unstable learners, which are algorithms whose output can change significantly with small changes in the training data. Examples of such learners include decision trees. By averaging the predictions of multiple instances of an unstable learner trained on different bootstrap samples, the variance of the ensemble is reduced, leading to improved generalization performance. The computational cost of selfbagging is higher than training a single model, as it requires training multiple models. However, the gains in accuracy often justify this additional computational overhead.