There are a rich variety of ways in which we can both transform and construct new features to make our models work more efficiently and give more accurate results. In general, there are no hard and fast rules for deciding which of the methods to use for a particular model. Much depends on the feature types (quantitative, ordinal, or categorical) that you are working with. A good first approach is to normalize and scale the features, and if the model requires it, transform the feature to an appropriate type, as we do through discretization. If the model performs poorly, it may be necessary to apply further preprocessing such as PCA. In the next chapter, we will look at ways in which we can combine different types of models, through the use of ensembles, to improve performance and provide greater predictive power.