Processing and Comparison of GBoost, XGBoost, and Random Forest in Titanic Survival Prediction

Abstract. Contemporarily, the machine learning has evolved from its early concepts to a sophisticated field consisting of advanced algorithms and diverse applications. Tree-based classification models have become powerful tools for complex predictive challenges. In this study, the effectiveness of tree-based classification models, such as Random Forest, XGBoost, and Gradient Boosting, is examined on the Titanic survival prediction challenge, which originates from the 1912 Titanic disaster. Passengers survival and death were influenced by various factors in this disaster. By using features such as gender, age, and class, and the survival outcome as the target variable, a binary classification model is developed to predict each passenger's survival status. The study includes data preprocessing, feature selection based on a foundational model, and model training. After the construction, hyper-parameters tuning, and cross-validation of three classifiers, this research compares and analyzes the performance scores to evaluate the characteristics of these tree-based learning methods, aiming to provide a reference for the similar applications.