18852 26700 sklearn.impute._base.SimpleImputer sklearn.SimpleImputer sklearn.impute._base.SimpleImputer 26 openml==0.12.2,sklearn==0.22.2.post1 Imputation transformer for completing missing values. 2021-07-06T13:15:55 English sklearn==0.22.2.post1 numpy>=1.11.0 scipy>=0.17.0 joblib>=0.11 add_indicator boolean false 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. copy boolean true 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 fill_value string or numerical value null 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 missing_values number NaN The placeholder for the missing values. All occurrences of `missing_values` will be imputed strategy string "median" 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 "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 verbose integer 0 Controls the verbosity of the imputer openml-python python scikit-learn sklearn sklearn_0.22.2.post1