TD_SimpleImputeTransform substitutes specified values for missing values in the input table. The specified values come from TD_SimpleImputeFit output.
The TD_SimpleImputeTransform function is a transformer for handling missing data in Teradata tables or views. TD_SimpleImputerTransform can be used to impute missing values with reasonable estimates, which ensures the resulting analysis or modeling is more accurate and reliable. This function is used with output from the TD_SimpleImputeFit function.
It can be applied to columns in a table or view that have missing values. TD_SimpleImputeTransform can replace these missing values with a variety of imputation strategies, including mean, median, most frequent, and constant values.
For example, suppose you have a dataset of customer information where each row represents a customer and columns represent various features such as age, income, gender, education level, etc. Some of the customers did not provide their income information during the data collection process, resulting in missing values in the income column. If you remove these rows with missing values, you will lose valuable information about these customers and potentially bias the analysis.
Use TD_SimpleImputeTransform to impute the missing values in the income column. For instance, you can use the mean or median income of the non-missing values in the same column to fill in the missing values. This imputed data helps to provide a complete picture of the dataset to perform further analysis, such as identifying income distribution among different genders, education levels, or age groups.