Difference Between Random Forest vs XGBoost
Feature |
Random Forest |
XGBoost |
---|---|---|
Model Building | Ensemble learning using independently built decision trees. | Sequential ensemble learning with trees correcting errors of previous ones. |
Optimization Approach |
Makes predictions by averaging individual tree outputs. |
Employs gradient boosting to minimize a loss function and improve accuracy iteratively. |
Handling Unbalanced Datasets |
Can struggle a bit |
Handles it like a pro |
Ease of Tuning |
Simple and straightforward |
Requires more practice but offers higher accuracy |
Adaptability to Distributed Computing |
Works well with multiple machines |
Needs more coordination but can handle large datasets efficiently |
Handling Large Datasets |
Can handle them but may slow down with very large data |
Built for speed, perfect for big datasets |
Predictive Accuracy |
Good, but not always the most precise |
Superior accuracy, especially in tough situations |
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