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I have a dataset of 100 patients and 1500 features. I split 80 train 20 test first and then use the train set to get the best hyperparameters / best feature subset doing the following:

I randomly split the train set into 70% train 30% "test" (I don't use it) X times and pass it through a classifier (Random Forest, L1 Logistic Reg, etc). Then, I take the most important features (from feature importance in RF or weights in LR) passing a certain threshold (mean/median of weights/importance). For each split, I save this list and make a counter. After X iterations, I have a counter like {feature 1: Y times, feature 2: Z times, etc}. Then I perform backwards elimination on the top features using the complete train set (the 80% of the complete dataset) and use K-fold stratified CV to find the hyperparameters for each BE iteration.

My mainly concern lies in the feature counter I am creating. I am using 70% of 80% of the total dataset to get these features. Note that I never use the 20% test set I set aside at the very beginning. Am I overfitting at any step?

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Step 1: Split the data base in 80/20 (you do not use 20 for anything now)

Step 2: The 80 of step 1, you split in 70/30.

Step 3: You use the 70/30 of step 2 to find the most importat features.

Step 4: You do k-fold cv using 80/20 to choose the best model.

I think You are doing great in terms of spliting the database.

You may also consider to choose your features using a procedure like this:

1) Permutation importance: The permutation feature importance is defined to be the decrease in a model score when a single feature value is randomly shuffled

2) Shap values: It is not an easy concept since it is based in game theory, but it shows the importance of each feature.

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  • $\begingroup$ In step 3, I am splitting the train test into 70-30 X times. That means that at the end I do use all the train set to find the subset of features. In step 4, I do k-fold cv on the 80% train (i.e. all data points from step 3) $\endgroup$ – Luis Pinto Feb 26 at 23:10
  • $\begingroup$ I cannot see any problem in your choice. It is not just very clear how you choose your best features. $\endgroup$ – DanielTheRocketMan Feb 26 at 23:15
  • $\begingroup$ For every 70% of the train set, I pass it through a classifier and take the features passing a feature threshold (I use weights or feature importance) and create a counter out of these lists. I use "SelectFromModel" function from sklearn.feature_selection package. $\endgroup$ – Luis Pinto Feb 26 at 23:20
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    $\begingroup$ It is ok. An option to do that ... See the edition. Just a minute. $\endgroup$ – DanielTheRocketMan Feb 26 at 23:23

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