| Issue | #Downvotes for this reason | By |
|---|
| axis | The axis along which to impute - If `axis=0`, then impute along columns - If `axis=1`, then impute along rows | default: 0 |
| 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 sparse and `missing_values=0`; - If `axis=0` and X is encoded as a CSR matrix; - If `axis=1` and X is encoded as a CSC matrix. | default: true |
| missing_values | The placeholder for the missing values. All occurrences of `missing_values` will be imputed. For missing values encoded as np.nan, use the string value "NaN" | default: "NaN" |
| strategy | The imputation strategy - If "mean", then replace missing values using the mean along the axis - If "median", then replace missing values using the median along the axis - If "most_frequent", then replace missing using the most frequent value along the axis | default: "mean" |
| verbose | Controls the verbosity of the imputer | default: 0 |