XGBoost: Optimized Gradient Boosting Trees for Enhanced Machine Learning Accuracy

1 Algorithm Introduction

XGBoost, short for eXtreme Gradient Boosting, is an ensemble learning algorithm based on Gradient Boosting Decision Trees (GBDT). It improves upon GBDT by introducing regularization terms and second-order derivative information, enhancing model performance and generalization ability.

As an efficient ensemble learning algorithm, XGBoost effectively utilizes multi-core processors for parallel computing, accelerating the model training process; it employs pruning techniques to reduce tree size, lower model complexity, and improve generalization ability; it uses Taylor expansion to approximate the loss function, allowing for better data understanding and faster convergence to optimal solutions. Due to these advantages, XGBoost is commonly used to solve problems in classification, regression, ranking, anomaly detection, and model interpretation.

2 Algorithm Principles

The core idea of the XGBoost model is to combine multiple weak classifiers (decision trees) into a strong classifier. Each decision tree is trained based on the residuals of the previous tree, iteratively optimizing the loss function to gradually reduce the residuals. At the same time, the model reduces the risk of overfitting by controlling the complexity of the trees and the regularization terms.

The principles of XGBoost are based on the gradient boosting algorithm, which iteratively adds prediction trees, with each tree attempting to correct the errors of the previous tree. Key features of XGBoost include:

(1) Second-order Taylor expansion: XGBoost uses second-order Taylor expansion in optimizing the loss function, making the algorithm more precise in handling non-linear problems.

(2) Regularization: To prevent overfitting, XGBoost incorporates regularization terms into the objective function to control model complexity.

(3) Missing value handling: XGBoost can automatically handle missing values in the data by learning the optimal split points to manage missing data.

(4) Parallelization: XGBoost supports parallel processing of feature dimensions, improving the training efficiency of the algorithm.

The construction of the XGBoost model typically includes the following steps:

(1) Data preprocessing: Initially, the raw data needs to be cleaned and preprocessed. This includes handling missing values, dealing with outliers, feature selection, and data normalization.

(2) Splitting training and testing sets: To evaluate the model’s performance, the dataset needs to be divided into training and testing sets. Typically, 80% of the data is used for training, and 20% for testing.

(3) Parameter tuning: The XGBoost model has many adjustable parameters, such as learning rate, number of trees, and tree depth. Techniques like cross-validation and grid search can be used to find the optimal parameter combination.

(4) Training the model: The model is trained using the training set. The XGBoost model gradually optimizes the classifier based on the defined loss function, generating multiple decision tree models.

(5) Model evaluation: The trained model is evaluated using the testing set. Common evaluation metrics include accuracy, precision, recall, and F1-score.

(6) Model application: Once the model is trained and validated, it can be applied to actual clinical data for prediction and decision support.

3 Algorithm Applications

XGBoost has a wide range of applications across various fields, including financial risk control (credit card fraud detection, loan approval), recommendation systems (product recommendations, news recommendations), and biomedicine (gene expression data analysis, building precise models for disease diagnosis), etc. In the field of traditional Chinese medicine, XGBoost is often used in recurrence prediction studies. Researchers such as Hao Ruofei have constructed XGBoost models to predict recurrence in patients with ischemic stroke undergoing traditional Chinese medicine treatment, while also building logistic regression (LR), support vector machine (SVM), Gaussian process model (GBM), decision tree (DT), and random forest (RF) models. The study included 48 patients with ischemic stroke from March 2019 to June 2022 at the Huguosi Traditional Chinese Medicine Hospital affiliated with Beijing University of Chinese Medicine, divided into recurrence and non-recurrence groups, comparing the differences in various indicators between the two groups, constructing recurrence risk prediction models, and including variables with inter-group differences of P<0.1 as risk variables. The sample cases were randomly divided into training and testing sets in a 7:3 ratio for model training and validation. Based on the predicted false positive rate, false negative rate, and overall accuracy, ROC curves were drawn to calculate AUC, sensitivity, and specificity, selecting the model with the best predictive performance.

The results showed: (1) Among the predictions of recurrence within 6 months, the LR model had the lowest accuracy, while the XGBoost model had the highest accuracy; the prediction accuracy for recurrence within 12 months was the same as that for 6 months, with the LR model being the lowest and the XGBoost model the highest (see Tables 3 and 4). (2) In the ROC curve analysis of model prediction performance, the predictions of recurrence within 6 months showed that all six models had AUCs >0.9, indicating good predictive performance, with the XGBoost model performing the best, having AUC, sensitivity, and specificity all >0.9; for predictions of recurrence within 12 months, the predictive performance of the six models decreased compared to the 6-month predictions, with DT and LR models having AUCs <0.9; the XGBoost model had the best predictive performance, with sensitivity and specificity close to 0.9, overall predictive performance superior to other models (see Table 5). This demonstrates that under traditional Chinese rehabilitation treatment, the XGBoost model has good predictive performance for patient recurrence within both 6 and 12 months.

4 Conclusion

XGBoost is a powerful and flexible machine learning algorithm that constructs a series of decision trees using gradient boosting, with each tree attempting to reduce the residuals of the previous tree. The regularization terms and second-order Taylor expansion are its core technologies, enabling it to achieve excellent performance across various datasets. Additionally, its effective handling of missing values and support for parallelization make it more efficient in processing large-scale data. Although XGBoost may present challenges in parameter tuning and computational resource requirements, it remains one of the most popular algorithms in the field of machine learning.

References:

[1] Hao Ruofei, Zhao Song. Research on Recurrence Prediction of Ischemic Stroke Patients Treated with Traditional Chinese Medicine Based on XGBoost Model [J]. China Drug Application and Monitoring, 2023, 20(6): 441-447.

[2] XGBoost Extreme Gradient Boosting (Part 1) – CSDN Blog. Accessed on September 11, 2024.

https://blog.csdn.net/qq_49183286/article/details/127336555.

[3] Detailed Explanation of XGBoost (Principle Part) – CSDN Blog. Accessed on September 11, 2024.

https://blog.csdn.net/weixin_55073640/article/details/129519870.

[4] Xgboost (eXtreme gradient boosting) – Zhihu. Accessed on September 11, 2024.

https://zhuanlan.zhihu.com/p/626482672.

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