| Issue | #Downvotes for this reason | By |
|---|
| add_indicator | If True, a :class:`MissingIndicator` transform will stack onto the output of the imputer's transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won't appear on the missing indicator even if there are missing values at transform/test time | default: false |
| copy | If True, a copy of X will be created. If False, imputation will be done in-place whenever possible | default: true |
| keep_empty_features | If True, features that consist exclusively of missing values when `fit` is called are returned in results when `transform` is called The imputed value is always `0` .. versionadded:: 1.2 | default: false |
| metric | default: "nan_euclidean" | |
| missing_values | The placeholder for the missing values. All occurrences of `missing_values` will be imputed. For pandas' dataframes with nullable integer dtypes with missing values, `missing_values` should be set to np.nan, since `pd.NA` will be converted to np.nan | default: NaN |
| n_neighbors | Number of neighboring samples to use for imputation weights : {'uniform', 'distance'} or callable, default='uniform' Weight function used in prediction. Possible values: - 'uniform' : uniform weights. All points in each neighborhood are weighted equally - 'distance' : weight points by the inverse of their distance in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away | default: 5 |
| weights | default: "uniform" |