Machine Learning and Artificial Intelligence with XGBoost Algorithm for Binary Classification
Abdelkhalak El Hami · 2025
Artificial intelligence is a computer science discipline aiming to endow machines with intellectual capacities similar to those of human beings. This chapter applies the machine learning XGBoost (Extreme Gradient Boosting) algorithm to a practical case study focusing on machine failure prediction. It starts by describing the dataset used, offering a detailed description of the key features and the target variable related to the machine failure indicators. It then implement the XGBoost algorithm for predictive modeling, by refining the hyperparameters to optimize the performances using cross-validation techniques. Model performances are evaluated using measures such as accuracy, precision, return and receiver operating characteristic curve. Moreover, the results of the model are interpreted by analyzing the feature importance and providing explanations on the predictions. The chapter presents the methodology used to develop and validate a predictive model based on XGBoost to solve a binary classification problem.