Univariate feature selection is a powerful technique to improve the performance of your models and to reduce their computational cost.
This feature selection technique uses statistical tests to assess the relationship between each input feature and the output feature. Input features with a strong statistical relationship with the output feature are kept. The remaining features are excluded.
In the following notebook, you’ll learn how to use univariate feature selection. Explore this notebook, do your own variations, and get better at feature selection. You can access all the data and the GitHub version here.