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I am working on a classification problem. I have two models:

  1. Logistic regression model

  2. Random Forest model

For the first model, if I choose the only predictors with p-values<0.05 I will reduce the accuracy, recall, etc. The IV (and WOE) predictors selection are not the same as the predictors that comply with the p-value rule.

On the other hand, Random Forest does not give p-values, only importance metrics, which are similar the IV predictors.

So, which methods should use to select the right predictors so I can compare the two models?

Thanks for reading this and I hope you can help me. I will be glad to give more details.

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  • $\begingroup$ What is the goal of your analysis? $\endgroup$
    – Dave
    Aug 19 at 20:40
  • $\begingroup$ I want to predict clients that they are going to upsell next month. $\endgroup$ Aug 22 at 9:20
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I recommend finding the best estimators for each model using a validation set and then comparing the final models, each one with different predictors, on the same dataset.

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