Implementation and Performance Comparison of Gradient Boosting Algorithms for Tabular Data Classification

In the field of machine learning, classification problems for tabular data are common and very important. This study looks at how three well-known gradient boosting algorithms—XGBoost, LightGBM, and CatBoost—are used to sort tabular data into different categories. Because these algorithms are used so often in data science and real life, it is important to give them a full review to help researchers and professionals pick the best one for their needs. The first step in our study is a deep look into the main ideas and optimization of the algorithms in question. Next, we put our methods to the test in both binary and multiclass classification settings on a large number of tabular datasets from different fields. Through our analysis, we can see that all three methods have pros and cons. As one of the first gradient boosting methods, XGBoost always does better than its rivals in terms of accuracy and durability. LightGBM shines when there are not many computing resources available because it is known for being very good at using them efficiently. CatBoost does well with datasets that have a lot of categorical factors with a high cardinality because of the way it handles these features. We also pay attention to training and research times to find out how much computer the algorithms need. Our research shows that although it takes a long time to train, CatBoost still has the fastest prediction performance. For immediate use, XGBoost and LightGBM are the best options. In the last part of this study, the performance of XGBoost, LightGBM, and CatBoost in solving tabular data classification problems is examined in detail. Doctors can easily choose the appropriate method based on their needs, including the tools they have access to and the content of the data they work with. The detailed results of this study show that the gradient boosting method can be used as a more efficient and effective classification solution. These techniques can be used to solve many machine learning problems.