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It is well known that when you do feature selection, there are statistical tools for comparing features of the same type if you know the output, for example

  1. Numerical data - > numerical output: Pearson's correlation coefficient /Spearman's rank coefficient
  2. Numerical data - > categorical output: anova/kendall's rank coefficient
  3. Categorical data->numerical output: anova/kendall's rank coefficient
  4. Categorical data-> categorical output: Chi squared / mutual information

but in ML you very commonly have mixtures of categorical and numerical features. How do you pick one group of categorical and numerical values from a large group of features?

Also how would you do feature extraction on mixed data?

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