An Experimental Comparison of Batch and Online Machine Learning Algorithms

This chapter presents the results of the experimental analyses. The first study (Sect. 9.1) examines the use of Batch Machine Learning (BML) and Online Machine Learning (OML) models for predicting the demand for bicycles at a bike-sharing station. The second study (Sect. 9.2) investigates the use of BML and OML models for prediction when very large data sets are available and drift is present. The synthetic Friedman-drift data set (see Definition 1.8 ) is used for this purpose. All data sets were standardized using the StandardScaler method so that the models were trained on data with mean zero and standard deviation one. In Sect. 9.3, we conducted a comprehensive investigation to evaluate the efficacy of scaling techniques in the context of drifting events. Our primary hypothesis centered on the potential benefits of scaling in handling dynamic data streams. Through rigorous experimentation and analysis, we compared various scaling methods to determine if one specific approach outperforms others in adapting to evolving data distributions.