What is the difference between bagging and bootstrapping in ML statistics?
Bagging (Bootstrap aggregating) and bootstrapping are two widely used ensemble learning techniques in machine learning. Both methods involve creating multiple subsets of the training data and training a model on each subset. However, they differ in how these subsets are created.
Bagging:
- Creates multiple subsets of the training data by randomly sampling with replacement.
- Each subset is used to train a separate model.
- The predictions from all the models are combined to make a final prediction.
Bootstrapping:
- Creates multiple subsets of the training data by randomly sampling without replacement.
- Each subset may contain duplicate instances from the original dataset.
- One model is trained on each subset.
- The final prediction is typically made by averaging the predictions from all the models.
Key Differences:
- Sampling: Bagging allows for duplicate instances in the subsets, while bootstrapping does not.
- Number of Subsets: Bagging typically uses more subsets than bootstrapping.
- Model Predictions: Bagging combines predictions from all models, while bootstrapping averages them.
- Variance: Bagging reduces variance more effectively than bootstrapping.
- Bias: Bootstrapping can introduce bias due to the duplication of instances.
Related Questions:
- What is the primary difference between bagging and bootstrapping? (Answer: The way they create subsets of the training data.)
- How does bagging improve the accuracy of the model? (Answer: By reducing variance.)
- What is the advantage of bootstrapping over bagging? (Answer: It is less computationally expensive.)
- Which technique is better suited for handling datasets with high variance? (Answer: Bagging)
- What is a key disadvantage of bootstrapping? (Answer: It can introduce bias.)
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