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I have data with 245 Features and almost all of the features are categorical. I would like to know what will be the best approach to find the important features for training the model. I know I can use tree approach for finding important features, but is there any other way around this?

This might sound vague, but would love to know your input.

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  • $\begingroup$ This is called Feature Selection. There are a ton of articles dealing with this issue. In all ways, you'd have to transform your variables before making selection so they're understandable by your model (from categorical to numerical if you use a logistic regression for example) $\endgroup$
    – Adept
    Sep 27 at 10:08
  • $\begingroup$ lgbm might give you an idea of importance without having to resort to boruta-shap. $\endgroup$
    – lcrmorin
    Oct 7 at 15:32
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If this is for classification task you can try Chi-squared test.

https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html

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    $\begingroup$ Your answer could be improved with additional supporting information. Please edit to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers in the help center. $\endgroup$
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    Sep 27 at 4:44

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