What distinguishes bagging from boosting, and how do they impact the bias and variance of a model?

Question

Anonymous · Accepted Answer

1. Bagging (Bootstrap Aggregating): Train many base models independently on
    bootstrap samples of the data; average (regression) or majority vote
    (classification). Goal: stabilize/denoise an unstable, high-variance learner
    (e.g., deep trees).

2. Boosting: Train base models sequentially, each one focusing on the previous
    models’ residuals/mistakes (or reweighted data). Combine them in a weighted
    sum. Goal: turn weak, high-bias learners (e.g., stumps) into a strong
    learner.

Anonymous · Answer

Random Forest and XGBoost are both ensemble learning methods, but they approach
the problem differently.
Random Forest uses a technique called bagging. It trains many decision trees
independently on random subsets of the data and features, then combines their
results—using majority vote for classification or averaging for regression. This
helps reduce overfitting and makes the model more stable, though it may still
have some bias if the trees are not deep enough.
XGBoost, on the other hand, uses boosting. It builds trees one after another,
with each new tree learning from the mistakes of the previous one. This
sequential learning process helps correct errors and often results in higher
accuracy. However, it can be sensitive to noisy data and overfitting if not
carefully regularized.
In short, Random Forest is great for simplicity and stability, while XGBoost
often delivers higher performance in complex tasks—but requires more tuning.

Anonymous · Answer

They are both ensemble models. Random forest is a bagging example that it use a
subset of data to train each tree and then aggregate the result to get the final
prediction (e.g. majority vote on classification problem and average for
regression problem). XGboost is a boosting example that it build tree
sequentially, each tree correct the prediction error of the previous one.
Bagging method can be bias with less trees or less split in each tree, causing
models not able to classify patterns correctly. Bagging methods is good for
reducing variance, that it combines all the trees to reduce the error. Boosting
method is less prone to bias but variance when it build many trees and keep
correcting errors. In the later trees, error can be due to outliers/

What distinguishes bagging from boosting, and how do they impact the bias and variance of a model?

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