Feature selection methods are used for selecting features that are likely to help with predictions. The following are the three methods for feature selection:
- Removing dummy features with low variance
- Identifying important features statistically
- Recursive feature elimination
When building predictive analytics models, some features won't be related to the target and this will prove to be less helpful in prediction. Now, the problem is that including irrelevant features in the model can introduce noise and add bias to the model. So, feature selection techniques are a set of techniques used to select the most relevant and useful features that will help either with prediction or with understanding our model.
