Random Forest vs XGBoost: Performance and Speed
- Random Forest can be slow in training, especially with a very large number of trees and on large datasets because it builds each tree independently and the full process can be computationally expensive. However, prediction is fast, as it involves averaging the outputs from all the individual trees.
- XGBoost is optimized for speed and performance. It is designed to be highly efficient and can handle large-scale data better than Random Forest. Its ability to run on multiple cores and even on distributed systems (like Hadoop) enhances its speed capabilities. The algorithm is optimized to do more computation with fewer resources. XGBoost models exhibit superior accuracies on test data, which is crucial for real-world applications. In scenarios where predictive ability is paramount, XGBoost holds a slight edge over Random Forest. This advantage is particularly noticeable in tasks requiring high precision. XGBoost demonstrates better performance than Random Forest in situations with class imbalances.
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