Random Forest vs XGBoost: Handling Overfitting
- Random Forest is less likely to overfit than a single decision tree because it averages multiple trees to give a final prediction, which generally leads to better generalization. Overfitting is further controlled by the randomness introduced through selecting random subsets of features to split on at each node.
- XGBoost includes several parameters which help prevent overfitting. The built-in regularization (L1 and L2) in XGBoost is a key feature that helps reduce overfit risk, not typically present in Random Forest. The parameters include:
max_depth
(to control the depth of the trees)- and
min_child_weight
(the minimum sum of instance weight needed in a child).
Difference Between Random Forest and XGBoost
Random Forest and XGBoost are both powerful machine learning algorithms widely used for classification and regression tasks. While they share some similarities in their ensemble-based approaches, they differ in their algorithmic techniques, handling of overfitting, performance, flexibility, and parameter tuning. In this tutorial, we will understand the distinctions between these algorithms for selecting the most appropriate one for a given task.
Table of Content
- What is Random Forest ?
- What is XGBoost?
- Algorithmic Approach
- Handling Overfitting
- Performance and Speed
- Use Cases
- Difference Between Random Forest vs XGBoost
- When to Use Random Forest
- When to Use XGBoost