Bagging
Definition of Bagging
Bagging is a technique for improving the accuracy of predictions made by a machine learning algorithm. It works by training multiple models on different subsets of the data, and then combining the predictions of these models using a voting scheme.
What is Bagging used for?
Bagging, or bootstrap aggregating, is a powerful machine learning and data science technique used to improve the accuracy of predictive models. It is an ensemble method that uses multiple learners on different samples of data to reduce overfitting and improve the accuracy of predictions. Specifically, bagging works by randomly sampling with replacement from a training dataset and then building a separate model for each sample. Each model is then combined into a single prediction or estimate. Bagging can be applied to any type of predictive model, including decision trees, regression models, and neural networks.
The primary benefit of bagging is that it helps reduce variance in the predictions from individual models in order to produce more accurate estimates overall. By randomly sampling different subsets of data for each model and combining their results into one estimate, bagging helps to reduce overfitting caused by individual models focusing too heavily on patterns specific to just one subset of the data. This can lead to better generalization capabilities when deploying models in real-world scenarios. Furthermore, bagging can also help reduce bias compared to some other techniques such as feature selection which may select only certain features that are biased towards one outcome or another.
Bagging can also be used as an effective way to improve the performance of less powerful algorithms or techniques such as Naive Bayes classifiers which struggle with complexity but generally have higher accuracy when using bagging techniques since they are able to learn patterns across multiple samples rather than learning from limited complex datasets.
Overall, Bagging is an effective tool for improving the accuracy and performance of predictive models while simultaneously reducing both bias and variance in predictions compared to non-bagging methods.