Bagging and boosting are two types of ensemble learning techniques used in machine learning to improve the performance of models and reduce errors. Ensemble learning is a method of combining multiple models to improve the accuracy and robustness of the predictions.

Bagging: Bootstrap Aggregating, also known as bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It decreases the variance and helps to avoid overfitting. It is usually applied to decision tree methods. Bagging is a special case of the model averaging approach. In bagging, multiple models are trained on different subsets of the training data, and their predictions are combined to make the final prediction. Each model is trained independently, and the final prediction is made by averaging the predictions of all the models. Bagging is usually applied where the classifier is unstable and has a high variance.

Boosting: Boosting is also an ensemble learning technique that combines multiple models to improve the accuracy of predictions. It is a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm. Boosting aims to reduce bias and avoid underfitting the data. In boosting, multiple models are trained sequentially, and each model is trained to correct the errors of the previous model. The final prediction is made by combining the predictions of all the models. Boosting is usually applied where the classifier is stable and simple and has high bias.

Differences between Bagging and Boosting:

• Bagging creates independent models that are aggregated together, while boosting updates the existing model with the new ones in a sequence.
• Bagging is a method of merging the same type of predictions, while boosting is a method of merging different types of predictions.
• Bagging decreases variance, not bias, and solves overfitting issues in a model, while boosting decreases bias, not variance.
• In bagging, each model receives an equal weight, while in boosting, models are weighed based on their performance.
• Models are built independently in bagging, while new models are affected by a previously built model’s performance in boosting[[3]](https://stats.st...