feature selection

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filtering - select based on summary statistic

  • ranks features or feature subsets independently of the predictor
  • univariate methods (consider one variable at a time)
    • ex. variance threshold
    • ex. T-test of y for each variable
    • ex. correlation screening: pearson correlation coefficient - this can only capture linear dependencies
    • mutual information - covers all dependencies
    • ex. chi$^2$, f anova
  • multivariate methods
    • features subset selection
    • need a scoring function
    • need a strategy to search the space
    • sometimes used as preprocessing for other methods

wrapper - recursively eliminate features

  • uses a predictor to assess features of feature subsets
  • learner is considered a black-box - use train, validate, test set
  • forward selection - start with nothing and keep adding
  • backward elimination - start with all and keep removing
  • others: Beam search - keep k best path at teach step, GSFS, PTA(l,r), floating search - SFS then SBS

embedding - select from a model

  • uses a predictor to build a model with a subset of features that are internally selected
  • ex. lasso, ridge regression, random forest