Ensemble Learning Techniques: Bagging vs. Boosting — A Comparison

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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. These subsets are created through bootstrapping, a process of sampling with replacement. The final prediction is then determined by averaging (for regression problems) or voting (for classification problems) the predictions of each individual model.

2. Key Concepts:

1. Bootstrapping: Randomly sampling data points with replacement to create multiple subsets for training.
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.

3. Random Forest:

A popular example of bagging is the Random Forest algorithm, which builds a collection of decision trees. Each tree is trained on a different bootstrap sample, and the final prediction is determined by aggregating the predictions of individual trees.

II. Boosting: