# Is it possible to identify which features contribute to the precision and recall for each label in sklearn's classification report output?

Below is an example of classification report output by sklearn. Is there an easy way to identify which features contribute to the precision and recall improvement for each label? I can start with using a single feature to build a model and add features one by one to see how the precision and recall of each label improve in the classification report. But obviously, this approach is quite tedious and time consuming plus may not work.

              precision    recall  f1-score   support

C0       0.38      0.65      0.48      4484
C1       0.23      0.34      0.27      3617
C2       0.26      0.38      0.31      3221
C3       0.23      0.17      0.19      2535
C4       0.50      0.45      0.47      2370
C5       0.31      0.23      0.26      2161

accuracy                           0.32     26735
macro avg       0.35      0.22      0.24     26735
weighted avg       0.32      0.32      0.30     26735
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