I. Bagging (Bootstrap Aggregating):

1. Definition:

• Bagging is an ensemble learning technique that involves training multiple instances of a model on different subsets of the training data, created through bootstrapping (random sampling with replacement).

2. Parallel Training:

• Models are trained independently in parallel, making bagging suitable for parallel computing.

3. Diversity:

• The goal is to introduce diversity among the models, reducing overfitting and improving generalization.

4. Example Algorithm:

• Random Forest is a well-known bagging algorithm that builds a collection of decision trees, each trained on a different bootstrap sample.

II. Boosting:

1. Definition:

• Boosting is an ensemble learning technique that combines the predictions of weak learners sequentially. Each new model corrects errors made by its predecessors.

2. Sequential Training:

• Models are trained sequentially, with each new model…