Imputer¶
Replaces missing values in a data set with a computed value infered from the remained of the data set. If there are missing data in the data set, those needs to be removed or replaced first.
Documentation
Replaces missing values in a data set with a computed value infered from the remained of the data set. If there are missing data in the data set, those needs to be removed or replaced first.
Configuration:
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.
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 “constant”, then replace missing values with fill_value. Can be used with strings or numeric data.
New in version 0.20: strategy=”constant” for fixed value imputation.
Attributes:
statistics_
The imputation fill value for each feature. Computing statistics can result in np.nan values. During
transform(), features corresponding to np.nan statistics will be discarded.
Input ports:
- Output ports:
- modelmodel
Model
Definition
Input ports
Output ports
- model
model
Model
