Feature Selection: A Primer
4 months ago
- #machine-learning
- #feature-selection
- #statistics
- Feature selection is crucial for simplifying models and reducing training time by identifying the most relevant features.
- Different feature selection methods are categorized into Unsupervised and Supervised methods, with the latter including Wrapper, Filter, and Embedded methods.
- Filter methods are fast and easy, analyzing statistical relationships between features and the target variable.
- Levels of Measurement (Nominal, Ordinal, Interval, Ratio) dictate which feature selection methods are applicable.
- Pearson’s R measures linear relationships between continuous variables, using covariance and standard deviation.
- Kendall’s Tau and Spearman’s Rho measure ordinal or monotonic relationships, handling non-linear data better than Pearson’s R.
- Chi-Squared Test assesses independence between categorical variables, useful for nominal or ordinal data.
- Mutual Information detects any relationship type, making it versatile for various data types.
- ANOVA F-Score evaluates how well continuous features separate categorical target classes.
- Point-Biserial Correlation is a specialized method for binary targets with continuous features, providing directionality.