Reviewing filter-based feature selection methods
Filter-based methods independently pick out features from a dataset without employing any ML. These methods depend only on the variables' characteristics and are relatively effective, computationally inexpensive, and quick to perform. Therefore, being the low-hanging fruit of feature selection methods, they are usually the first step in any feature selection pipeline.
Two kinds of filter-based methods exist:
- Univariate: Individually and independently of the feature space, they evaluate and rate a single feature at a time. One problem that can occur with univariate methods is that they may filter out too much since they don't take into consideration the relationship between features.
- Multivariate: These take into account the entire feature space and how features within interact with each other.
Overall, for the removal of obsolete, redundant, constant, duplicated, and uncorrelated features, filter methods are very strong. However...
