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What are different statistical tests used for Feature selection in Machine Learning?

 

Feature TypeTest NameDescriptionUse Case
NumericalPearson's Correlation CoefficientDetermines the strength and direction of linear relationships between numerical variables. High absolute values indicate strong correlations.Measure linear correlation
NumericalMutual InformationMeasures the amount of information gained about one variable by observing another. Useful for feature selection when dealing with numerical data.Measure dependence between variables
NumericalANOVAAnalyzes the difference in means among multiple groups. Helpful for selecting numerical features with significant differences in group means.Compare means between multiple groups
Numericalt-testAssesses whether the means of two groups are statistically different. Useful for binary classification tasks.Compare means between two groups
CategoricalChi-Square TestDetermines if two categorical variables are independent or related. Useful for feature selection with categorical data.Test independence of categorical variables
CategoricalFisher's Exact TestTests the association between two categorical variables in 2x2 contingency tables. Applicable when sample sizes are small.Test independence in 2x2 contingency tables
CategoricalGini ImportanceMeasures how often a feature is used to split data in decision tree algorithms. Higher values indicate more important features.Assess feature importance in decision trees
CategoricalInformation GainCalculates the reduction in entropy (uncertainty) achieved by using a feature to split data in decision trees or random forests.Measure reduction in entropy
CategoricalCramér's VQuantifies the association between two categorical variables in contingency tables. Values range from 0 (no association) to 1 (complete association).Measure association in contingency tables
CategoricalKendall's Tau and Spearman's Rank CorrelationEvaluate the strength and direction of monotonic relationships between ordinal or ranked data. Useful when data is not normally distributed.Measure rank correlation
CategoricalPoint-Biserial CorrelationAssesses the relationship between a binary target variable and a continuous or ordinal feature. Helps identify features with strong associations.Measure correlation with binary target
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