This chapter will cover the basics of predictive modeling, covering topics related to the mathematical machinery, types of predictive models, and tuning strategies. For many readers, prediction is the ultimate goal of their work, so it is important to understand that this topic is a full field of its own. Take this chapter as an introduction and launching-off point for your learning.
The following topics will be covered in this chapter:
- Mathematical machinery, including loss functions and gradient descent
- Linear regression and penalties
- Logistic regression
- Tree-based classification, including random forests
- Support vector machines
- Tuning methodologies including cross-validation and hyperparameter selection
