Feature engineering

Feature engineering is the process of transforming raw data into numerical values that can be used in machine learning algorithms. For example, we can transform raw data about software defects (for example, their description, the characteristics of the module they come from, and so on) into a table of numerical values that we can use for machine learning. The raw numerical values, as we saw in the previous chapter, are the result of quantifying entities that we use as sources of data. They are the results of applying measurement instruments to the data. Therefore, by definition, they are closer to the problem domain rather than the solution domain.

The features, on the other hand, quantify the raw data and contain only the information that is important for the machine learning task at hand. We use these features to make sure that we find the patterns in the data during training that we can then use during deployment. If we look at this process from the perspective...