Imputer Transform

Imputation estimator for completing missing values

Type

ml-predict

Class

fire.nodes.etl.NodeImputerTransform

Fields

Details

Imputer Transform Node Details

The Imputer Transform Node is used to impute missing values in a dataset. It takes in a fit model as input, which is typically the output of a previous Imputer Estimator Node, and uses it to fill in missing values in a DataFrame.

Input Parameters

FIT MODEL : The output of a previous Imputer Estimator Node, which contains the imputation strategy and parameters used for filling in missing values.

Examples

Imputer Transform Node Example

Consider the following example, where we have a DataFrame with columns ‘age’ and ‘income’ containing missing values. We use an Imputer Estimator Node to specify the imputation strategy as ‘mean’ and create a fit model. Then, we use the Imputer Transform Node to fill in the missing values in the ‘age’ and ‘income’ columns using the fit model.

Input DataFrame

id age income

1 22 null

2 null 35

3 45 45

4 55 null

Imputer Estimator Node

Imputation strategy = ‘mean’

Imputer Transform Node

Input = DataFrame, Fit Model

Output =

id age income

1 22 40

2 40 35

3 45 45

4 55 40

In this example, the input DataFrame contains missing values in the ‘age’ and ‘income’ columns. The fit model is created using the Imputer Estimator Node with the imputation strategy set to ‘mean’. The Imputer Transform Node uses this fit model to fill in the missing values.