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enhancementNew feature or requestNew feature or requestgood first issueGood for newcomersGood for newcomersquestionFurther information is requestedFurther information is requested
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We compute variable importance by calculating Pearson's correlation between scores and target encoded variables:
cobra/cobra/model_building/models.py
Lines 144 to 150 in 0133435
| importance_by_variable = { | |
| utils.clean_predictor_name(predictor): stats.pearsonr( | |
| data[predictor], | |
| y_pred | |
| )[0] | |
| for predictor in self.predictors | |
| } |
It'd be nice to choose different correlation (like Kendall)? Pearson assumes normality, but doesn't always hold for the variables considered.
https://datascience.stackexchange.com/questions/64260/pearson-vs-spearman-vs-kendall
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enhancementNew feature or requestNew feature or requestgood first issueGood for newcomersGood for newcomersquestionFurther information is requestedFurther information is requested