A data science team is developing a model to predict fraudulent financial transactions. They initially implemented a standard gradient boosting model but are experiencing issues with overfitting and long training times on their large, high-dimensional dataset. To address these challenges, the team decides to switch to XGBoost. Which of the following features inherent to XGBoost provides a direct mechanism to combat overfitting by penalizing model complexity, a technique not standard in traditional gradient boosting implementations?
Built-in L1 (Lasso) and L2 (Ridge) regularization.
Parallel processing and cache-aware access.
Use of second-order derivatives (Hessian) in the objective function approximation.
Intrinsic handling of missing values through sparsity-aware split finding.
The correct answer is the inclusion of built-in L1 (Lasso) and L2 (Ridge) regularization. XGBoost enhances the standard gradient boosting algorithm by adding a regularization term to its objective function. This term penalizes the complexity of the model (specifically, the number of leaves and the magnitude of their scores), which helps to prevent overfitting, a common issue in complex models.
Parallel processing and cache-aware access is an incorrect option because these features are designed to improve computational speed and efficiency, not to directly control overfitting.
Intrinsic handling of missing values is incorrect as this feature, while beneficial for simplifying data preprocessing, does not serve as a regularization technique to prevent overfitting.
Use of second-order derivatives (Hessian) is incorrect because while XGBoost does use a second-order Taylor expansion of the loss function (which involves the Hessian matrix) to achieve faster convergence, this is part of the optimization algorithm itself. The regularization term is a separate component added to the objective function specifically to control model complexity and prevent overfitting.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What are L1 (Lasso) and L2 (Ridge) regularization in machine learning?
Open an interactive chat with Bash
How does XGBoost's regularization differ from standard gradient boosting?
Open an interactive chat with Bash
Why is overfitting a challenge in machine learning models, especially with complex datasets?