What is XGBoost?
XGBoost(Extreme Gradient Boosting) is a highly efficient and flexible gradient boosting algorithm that has gained popularity due to its speed and performance, especially in structured or tabular data. XGBoost builds trees sequentially, with each new tree correcting errors made by the previous ones, thus incrementally improving the model’s predictions. It incorporates a number of optimizations in model training and handling of data, including built-in regularization (L1 and L2) to prevent overfitting, and advanced features like handling missing values and pruning of trees.
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