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What are different statistical tests can be performed for Feature selection in Machine learning?

Feature selection in machine learning often involves the use of statistical tests to assess the significance of each feature or variable with respect to the target variable. The choice of statistical test depends on the type of data (categorical or numerical) and the nature of the problem (classification or regression). Here are some common statistical tests used for feature selection:

  • Numerical Features (Continuous Variables)


    • Correlation Test (Pearson's Correlation Coefficient)
    • Mutual Information
    • ANOVA (Analysis of Variance)
    • t-test


  • Categorical Features (Discrete Variables)


    • Chi-Square Test
    • Fisher's Exact Test
    • Gini Importance
    • Information Gain
    • Cramér's V
    • Kendall's Tau and Spearman's Rank Correlation
    • Point-Biserial Correlation
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