- feature selection (returns a subset of the features)
- and feature extraction (create new features that are functions of the original features)
This methods are some called “Model selection methods”.
They are an essential tool for data analysis, especially for big datasets involving many predictors.
In dimensionality reduction, the goal is to select/retain a subset of features while still retaining as much of the variance in the dataset as possible.
- random projections
- feature hashing