FEA Implement categorical feature support to IterativeImputer
#31479
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…e support Our implementation automatically detects categorical columns (based on dtype), uses a RandomForestClassifier to model and predict missing category labels, and then inverse-transforms those predictions back into the original categories. So, if given a certain X data, where X features are numerical and categorical, respectively, and there are values missing (nan) in the categorical one, _iterative.py, through the Random Forest Classifier, will predict those missing values and replace them in the data. Co-authored-by: Fabioprata23 <fabio.prata@tecnico.ulisboa.pt>
IterativeImputer
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Fixes #31219
Our implementation automatically detects categorical columns
(based on dtype), uses a RandomForestClassifier to model and predict missing category labels, and then inverse-transforms those predictions back into the original categories. So, if given a certain X data, where X features are numerical and categorical, respectively, and there are values missing (nan) in the categorical one, _iterative.py, through the Random Forest Classifier, will predict those missing values and replace them in the data.
Co-authored-by: Fabioprata23 fabio.prata@tecnico.ulisboa.pt
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