Training Approach of Gradient Boosting vs Random Forest
Gradient Boosting Trees (GBT):
- GBT trains trees sequentially, with each new tree trying to correct the errors made by the previous ones.
- The algorithm fits each new tree on the residual errors of the previous ensemble.
- This sequential training approach can lead to longer training times, especially for a large number of trees.
Random Forests:
- Random Forests train each tree independently.
- Each tree is built on a random subset of the training data and a random subset of features.
- This parallel training approach allows Random Forests to train faster, as each tree can be built independently of the others.
Gradient Boosting vs Random Forest
Gradient Boosting Trees (GBT) and Random Forests are both popular ensemble learning techniques used in machine learning for classification and regression tasks. While they share some similarities, they have distinct differences in terms of how they build and combine multiple decision trees. The article aims to discuss the key differences between Gradient Boosting Trees and Random Forest.
How is Gradient Boosting different from Random Forest?
- Basic Algorithm
- Training Approach
- Performance
- Interpretability
- Handling Overfitting
- Hyperparameter Sensitivity
- Computational Complexity
- Suitable for Large Datasets
- Feature Importance
- Robustness to Noise
- Gradient Boosting Trees vs Random Forests
- When to Use Gradient Boosting Trees
- When to Use Random Forests
Let’s dive deeper into each of the differences between Gradient Boosting Trees (GBT) and Random Forests: