Random Forest vs XGBoost: Algorithmic Approach
- Random Forest uses an ensemble technique known as bagging (Bootstrap Aggregating) which helps to improve stability and accuracy. To produce a prediction that is more reliable and accurate, it constructs several decision trees and combines them. Every tree in the ensemble is constructed using a bootstrap sample, which is a sample taken from the training set with replacement. Furthermore, Random Forest only takes into account a random subset of features for splitting at each node while constructing separate trees, increasing tree diversity and producing a more resilient model that is less prone to overfitting. .
- XGBoost develops one tree at a time, correcting faults caused by previously trained trees, in contrast to Random Forest, where each tree is generated independently and the results are aggregated at the end. Trees are planted until none remain. The model uses a gradient descent algorithm to minimize the loss when adding new models. This sequential addition of weak learners (trees) ensures that the shortcomings of previous trees are corrected. The additive model known as gradient boosting is implemented by XGBoost.
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