What is the difference between bagging and boosting?

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Bagging and boosting are two popular ensemble learning techniques used to improve the performance and accuracy of machine learning models by combining multiple weak learners—typically decision trees.

🌟 1. Bagging (Bootstrap Aggregating):

How it works:

• Trains multiple models independently on random subsets of the training data (with replacement).

• Final prediction is made by averaging (for regression) or majority voting (for classification).

Goal: Reduce variance and prevent overfitting.

Key Features:

• Models are trained in parallel

• Each model sees a different version of the data

• Robust to noisy data

Example: Random Forest (an ensemble of decision trees)

⚡ 2. Boosting:

How it works:

• Trains models sequentially, where each new model focuses on correcting the errors of the previous ones.

• Assigns more weight to misclassified instances.

Goal: Reduce bias and improve accuracy.

Key Features:

• Models are trained in sequence

• Later models are dependent on earlier ones

• Often more accurate but can overfit if not regularized

Examples:

• AdaBoost

• Gradient Boosting

• XGBoost, LightGBM, CatBoost

Read More

Explain decision trees and how they work.

What is regularization (L1 and L2)?

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