How does Bagging improve the robustness of models?

Bagging, short for Bootstrap Aggregating, improves the robustness of models by reducing variance and minimizing the impact of overfitting. It works by training multiple instances of the same base model on different subsets of the training data, which are created by sampling with replacement (bootstrap sampling). Each subset is used to train a separate model, and the final prediction is made by aggregating the predictions of all individual models, typically through voting (for classification) or averaging (for regression).

This approach enhances robustness in several ways:

1. Reduces Variance: Since each model is trained on a slightly different dataset, the overall ensemble is less sensitive to variations in the training data, leading to more stable predictions.
2. Mitigates Overfitting: By averaging multiple models, bagging cancels out the errors of individual models, especially those that overfit to noise in the training data.
3. Improves Generalization: The ensemble's collective decision is often more accurate and reliable than any single model, as it leverages diverse perspectives from different subsets of data.

Example: In a classification task, suppose you have a decision tree model that tends to overfit the training data. By applying bagging, you create 100 decision trees, each trained on a different bootstrap sample of the data. When making a prediction, you aggregate the votes of all 100 trees. If most trees predict "Class A," the final output will be "Class A," reducing the risk of a single tree's overfitting leading to incorrect predictions.

In cloud-based machine learning workflows, Tencent Cloud's Machine Learning Platform (TI-ONE) can be used to implement bagging efficiently. It provides tools for distributed training, model aggregation, and scalable deployment, enabling users to build robust ensemble models with minimal effort.