Reviewing filter-based feature selection methods
Filter-based methods independently select 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.
Filter-based methods can be categorized as:
- 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 interact with each other.
Overall, for the removal of obsolete, redundant, constant, duplicated, and uncorrelated features, filter...