Introduction to Machine Learning with Python: A Guide for Data Scientists

By Andreas Müller, Sara Guido. 2016

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A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, background): “In fact, Random Forest tends to work well with real data as it is more robust to noise, while Adaboost could fit noise into its learning and, therefore, produce overfitting [43,44].”
Mathematics for Machine Learning (2020, background): “…2010; Barber, 2012; Murphy, 2012; Shalev-Shwartz and Ben-David, 2014; Rogers and Girolami, 2016) or programmatic aspects of machine learning (Müller and Guido, 2016; Raschka and Mirjalili, 2017; Chollet and Allaire, 2018), we provide only four representative examples of machine learning…”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, unknown): “below) involves the definition of a learning problem to solve a task based on inputs [92].”