Boosting algorithms, particularly CatBoost, XGBoost, and LightGBM, have become essential for training on tabular data due to their ability to enhance predictive performance through ensemble learning. CatBoost, developed by Yandex, distinguishes itself with features like symmetric trees and ordered boosting to mitigate overfitting, native support for various data types, and efficient handling of categorical features via strategies like one-hot encoding and target encoding. It also offers sophisticated model analysis tools, such as SHAP and feature importance metrics, and excels in prediction speed and accuracy, especially in scenarios involving categorical data. Benchmark comparisons and hands-on experiments in flight delay prediction illustrate CatBoost's superior performance and speed compared to XGBoost and LightGBM, even with default parameters, making it a strong candidate for tasks requiring low latency and robust categorical data handling.