XGBoost (Extreme Gradient Boosting) and Random Forests are both powerful ensemble learning techniques, but they employ distinct methodologies.
Key Differences
Boosting vs. Bagging
XGBoost: Utilizes a boosting approach that iteratively builds trees to address the shortcomings of preceding ones.
Random Forests: Operates on a bagging strategy that constructs trees independently, and the ensemble averages their predictions.
Tree Building Mechanism
XGBoost: Employs a learning algorithm that incorporates
Early stopping
Regularization techniques such as
(LASSO) and
(ridge) to minimize overfitting.
Computational efficiency via split finding algorithms using approximate tree boosting.
Random Forests: Uses feature randomness and bootstrapping (sampling with replacement) to build multiple trees. Each tree considers a random subset of features at each split.
XGBoost Add-Ons
Bias and Variance Reduction: XGBoost offers better control over bias-variance tradeoff, allowing users to steer the model into a higher bias or variance regime according to their datasets and objectives.
Cross-Validation Integration: The library can embed cross-validation during the model's training process, making it more adaptable to varying dataset characteristics.
Integrated Shrinkage: Unified approach to shrinkage capabilities via "learning rate," which can contribute to model generalization and speed.
Verification Efficiency
XGBoost: Verification occurs on the most recent tree after each iteration, enhancing the process's efficiency.
Random Forests: Inspects all the trees, leading to a relatively slower operation.
Computational Efficiency
XGBoost: Leverages multiple threads and directs core utilization for parameter tuning and constructing decision trees, granting it a computational advantage.
Random Forests: Although computationally robust, its performance can reduce in high-dimensional settings when faced with an extensive list of input features.
Outlier Handling
XGBoost: Can be sensitive to extreme data points or outliers in certain scenarios.
Random Forests: With the ensemble averaging method, Random Forests are frequently more impervious to outliers.
Parallel Processing
XGBoost: Implies a parallel computational structure for tree construction, while in Random Forests, such an arrangement might be constrained to bootstrapping.
Random Forests: Limited parallelism due to the bootstrapping-based strategy for tree construction.
