What is difference between bagging and boosting

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Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models.

Bagging involves training multiple models on different subsets of the training data and then combining their predictions through voting or averaging.
Boosting involves training multiple models sequentially, with each subsequent model focusing on the samples that the previous models misclassified.
Bagging reduces variance and overfitting, while boosting reduces bias and underfitting.
Examples of bagging algorithms include random forest and bootstrap aggregating, while examples of boosting algorithms include AdaBoost and gradient boosting.
Both bagging and boosting can be used with various types of machine learning models, such as decision trees, neural networks, and support vector machines.

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