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Impute Missing Values for only one attribute using a subset of attributes
Newbie
Hello everyone!
The impute missing values operator does not seem to work the same way as it predicts the values for all selected attributes. Is there a way to only impute the missing values of one attribute using the model I created before? Alternatively, how can I increase the prediction quality of the model in the Imputation Operator?
Because I have many missing values in my dataset, I created a KNN model to predict the missing values for one attribute. The model works well so I want to use it to impute the missing values. Unfortunately, the model does not work in the same way when inserted into the impute missing values operator.
Before: I selected the subset of attributes from the data set that help to predict the missing values of an attribute. Then I use the Set Role Operator to the attribute that I want to predict. The model is then trained on the subset of attributes and predicts the missing values for only one variable (indicated through the Set Role Operator).
The impute missing values operator does not seem to work the same way as it predicts the values for all selected attributes. Is there a way to only impute the missing values of one attribute using the model I created before? Alternatively, how can I increase the prediction quality of the model in the Imputation Operator?
Thank you very much.
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