What is the difference between bagging and boosting?

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Bagging and boosting are both ensemble learning techniques that improve the performance of machine learning models by combining multiple models, but they differ in how they build and combine these models.

Bagging (Bootstrap Aggregating):

• Goal: Reduce variance and avoid overfitting.

• How it works: Creates multiple independent models by training them on different random subsets of the data (with replacement).

• Model training: Each model is trained in parallel.

• Final prediction: For classification, typically uses majority voting; for regression, averages the predictions.

• Example: Random Forest, which combines multiple decision trees trained on different bootstrap samples.

Boosting:

• Goal: Reduce bias and improve model accuracy.

• How it works: Trains models sequentially, with each model focusing on correcting the errors of the previous one.

• Model training: Each new model gives more weight to the instances that were misclassified by earlier models.

• Final prediction: Combines the models’ outputs using weighted voting or averaging.

• Example: AdaBoost, Gradient Boosting, XGBoost.

Key Differences:

• Parallel (bagging) vs. sequential (boosting) training.

• Bagging reduces variance, while boosting reduces bias.

• Bagging treats all models equally, while boosting gives more weight to better-performing models.

In short, bagging builds strong models by combining many “weak” models in parallel, while boosting does so sequentially, improving each step by learning from past mistakes.

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