| Issue | #Downvotes for this reason | By |
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| add_indicator | If True, a :class:`MissingIndicator` transform will stack onto 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. Note that, in the following cases, a new copy will always be made, even if `copy=False`: - If `X` is not an array of floating values; - If `X` is encoded as a CSR matrix; - If `add_indicator=True` | default: true |
| fill_value | When strategy == "constant", fill_value is used to replace all occurrences of missing_values If left to the default, fill_value will be 0 when imputing numerical data and "missing_value" for strings or object data types | default: null |
| 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 |
| strategy | The imputation strategy - If "mean", then replace missing values using the mean along each column. Can only be used with numeric data - If "median", then replace missing values using the median along each column. Can only be used with numeric data - If "most_frequent", then replace missing using the most frequent value along each column. Can be used with strings or numeric data If there is more than one such value, only the smallest is returned - If "constant", then replace missing values with fill_value. Can be used with strings or numeric data .. versionadded:: 0.20 strategy="constant" for fixed value imputation | default: "mean" |
| verbose | Controls the verbosity of the imputer | default: 0 |