What is Random Forest ?
Random Forest is an ensemble machine learning algorithm that operates by building multiple decision trees during training and outputting the average of the predictions from individual trees for regression tasks, or the majority vote for classification tasks. It improves upon the performance of a single decision tree by reducing overfitting, thanks to the randomness introduced during the creation of individual trees. Specifically, each tree in a Random Forest is trained on a random subset of the training data and uses a random subset of features for making splits.
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