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Python implementation of Sequential Forward Feature Selection from scratch.

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Sequential-Forward-Feature-Selection

Python implementation of Sequential Forward Feature Selection from scratch.

  • The program will take one input: a dataset where the last column is the class variable.
  • The program will load the dataset and then use the wrapper approach with a sequential forward selection strategy to find a set of essential features.
  • Stratified 5-fold cross-validation was used for measuring accuracy.
  • The program will keep adding the features as long as there is some improvement in the classification accuracy.
  • The output of the program will be the set of important features on the console.

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Python implementation of Sequential Forward Feature Selection from scratch.

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