Automatic point cloud classification (PCC) is a challenging task in large-scale urban point clouds due to the heterogeneous density of points, the high number of points and the incomplete set of objects. Although recent PCC studies rely on automatic feature extraction through deep learning (DL), there is still a gap for traditional machine learning (ML) models with hand-crafted features, particularly after emerging gradient boosting machine (GBM) methods. In this study, we are using the traditional ML framework for the problem of PCC in large-scale datasets following the steps of neighborhood definition, multi-scale feature extraction, and classification. Different from others, our framework takes advantage of the fast feature calculation with multi-scale radius neighborhood and a recent state-of-the-art GBM classifier, LightGBM. We tested our framework using three mobile urban datasets, Paris–Rau–Madame, Paris–Rue–Cassette and Toronto3D. According to the results, our framework outperforms traditional machine learning models and competes with DL-based methods.