In Chapter 2, we looked at selecting the variables for a model. The process is often called feature extraction or feature engineering.
An example of this would be a computer model that identifies a male or female from a photo. For humans, this is fairly easy and quick. It’s something that is intuitive. But if someone asked you to describe the differences, would you be able to? For most people, it would be a difficult task. However, if we want to build an effective machine learning model, we need to get feature extraction right—and this can be subjective.
Table 3-1 shows some ideas about how a man’s face may differ from a woman’s.
Table 3-1.
Facial features
| Features | Male |
|---|---|
| Eyebrows | Thicker and straighter |
| Face shape | Longer and larger, with more of a square shape |
| Jawbone | Square, wider, and sharper |
| Neck | Adam’s apple |
This just scratches the surface as I’m sure you have your own ideas or approaches. And this is normal. But this is also why such things as facial recognition are highly complex and subject to error.
Feature extraction also has some nuanced issues. One is the potential for bias. For example, do you have preconceptions of what a man or woman looks like? If so, this can result in models that give wrong results.
Because of all this, it’s a good idea to have a group of experts who can determine the right features. And if the feature engineering proves too complex, then machine learning is probably not a good option.
But there is another approach to consider: deep learning. This involves sophisticated models that find features in a data. Actually, this is one of the reasons that deep learning has been a major breakthrough in AI. We’ll learn more about this in the next chapter.
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