Fundamental concepts in this chapter
In this section, we’ll briefly cover concepts that provide additional context for this chapter’s learning activities.
Dimensions and features
We introduced the concept of features in Chapter 1 and described examples of features using the King County housing sales dataset for illustration. To briefly recap, features are individual, measurable properties or characteristics of the observations in our dataset. They are the aspects of our dataset from which a machine learning algorithm learns to create a model. In other words, a model can be seen as a representation of patterns learned by the algorithm from the features in our dataset.
The features of a house, for example, include information such as how many rooms it contains, the year it was constructed, where it is located, and other factors that describe the house, as depicted in Table 7.1:
Table 7.1: King County house sales features
When we’...
