In this module, we presented the principle of feature selection. In short, feature selection is not a magical tool to get marginal gains. We tackle the following aspects:
you should use feature selection to speed-up training and testing rather than seeking for marginal performance gains;
you should be careful regarding the framework and how to include a feature selector within your pipeline;
you should be aware of the limitation of a feature selector based on machine-learning models.
To go further#
You can refer to the following scikit-learn examples which are related to the concepts approached during this module: