In this chapter, we introduced the idea of (and purpose for) data dimensional reduction: reducing the total number of observations to consider when creating a predictive model.

The most common methods, strategies, and concepts for reduction were reviewed, including correlated data analysis, reporting on correlation, PCA, ICA, and factor analysis.

In the next chapter, we think about how several trained models can work together as an ensemble, in order to produce a single model that is more powerful than the individual models involved.