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.

• 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

class node_preprocessing.Imputer