Bagging and Boosting are two techniques used in ensemble learning to improve the accuracy of machine learning models. Ensemble learning is the process of constructing multiple models and combining their predictions to produce more accurate and robust results.
**Bagging** Bagging is short for Bootstrap Aggregating. In bagging, multiple models are trained on different subsets of the training data. These models are then used to make predictions independently. The final prediction is then determined by averaging the outputs of all the models for regression tasks, or taking the majority vote for classification tasks. Bagging is an example of parallel ensemble learning as each of the models are trained independently and in parallel. The goal of Bagging is to reduce the overfitting of the model.
**Boosting** Boosting is a technique where multiple models are trained in sequence, with each new model focusing on the errors of the previous model. The goal of boosting is to improve the accuracy and reduce the bias of the model. In boosting, every subsequent algorithm is designed to correct the errors made by the previous algorithms. The predictions from each model are combined by weighted voting, where each model is assigned a weight based on its performance.
**Differences** - In Bagging, multiple learners are trained independently and in parallel. In Boosting, learners are trained sequentially, and each learner focuses on the previous learner’s errors. - Bagging aims to reduce overfitting of the model, whereas boosting attempts to improve the bias-variance trade-off in order to improve the accuracy of the model. - With Bagging, the output is the average of all the outputs of the models, whereas in Boosting, the outputs of all the models are combined by weighted voting.
To illustrate the differences, let me give you an example. Suppose we have a dataset that contains 100,000 records. In Bagging, we randomly select 80,000 records from this dataset to train each of the models. The remaining 20,000 records are used for testing.
In Boosting, the first model is trained on the entire dataset. The subsequent models are trained on the errors made by the first model. The weight of each model is determined based on its performance, with better-performing models being given more weight.
In conclusion, Bagging and Boosting are two popular methods of ensemble learning used to improve the accuracy and robustness of machine learning models. Bagging trains multiple models independently in parallel, and their outputs are combined by averaging. Boosting, on the other hand, trains multiple models sequentially, and their outputs are combined by weighted voting.