Machine Learning Algorithms in Depth

7 Selected supervised learning algorithms

This chapter covers

• Markov models: page rank and HMM
• Imbalanced learning, including undersampling and oversampling strategies
• Active learning, including uncertainty sampling and query by committee strategies
• Model selection, including hyperparameter tuning
• Ensemble methods, including bagging, boosting, and stacking
• ML research, including supervised learning algorithms

In the previous two chapters, we looked at supervised algorithms for classification and regression. In this chapter, we focus on a selected set of supervised learning algorithms. The algorithms are selected to give exposure to a variety of applications—from time series models used in computational finance to imbalanced learning used ...