To end, it is important to mention that feature selection is not the same as dimensionality reduction techniques like Principal Component Analysis (PCA). Dimensionality reduction techniques generally reduce the number of features of your data by combining the initial features to form new ones, whereas feature selection techniques select a subset from the initial set of features without modifying them.
Conclusion and Other resources
Feature selection is a very important step in the construction of Machine Learning models. It can speed up training time, make our models simpler, easier to debug, and reduce the time to market of Machine Learning products. The following video covers some of the main characteristics of Feature Selection mentioned in this post.
Also, there are books on feature selection like which really go deep into the subject, in case you want to know A LOT more:
- Feature Selection for Knowledge Discovery and Data Mining (Springer)
- Feature Engineering and Selection: A practical approach for Predictive Models (Kuhn, Johnson)
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