Machine Learning with Swift

Choosing a good set of features

For ML purposes, it's necessary to choose a reasonable set of features, not too many and not too few:

• If you have too few features, this information may be not sufficient for your model to achieve the required quality. In this case, you want to construct new ones from existing features, or extract more features from the raw data.
• If you have too many features you want to select only the most informative and discriminative, because the more features you have the more complex your computations become.

How do you tell which features are most important? Sometimes common sense helps. For example, if you are building a model that recommends books for you, the genre and average rating of the book are perhaps more ...