What method is used for handling missing data in predictive modeling?

Imputation is a widely accepted method for handling missing data in predictive modeling because it allows analysts to estimate and replace missing values based on the information available in other data points. By applying statistical techniques, such as mean, median, or mode imputation, or more advanced approaches like regression imputation or k-nearest neighbors, analysts can maintain the integrity of the dataset while ensuring that the predictive models remain robust and usable.

This method is essential as missing data can lead to bias or inaccurate predictions, and simply discarding incomplete records might result in a loss of valuable information that could inform the model. Imputation helps to fill in the gaps so the analysis can proceed without compromising the dataset's overall quality.

Other methods listed, such as validation, are primarily focused on evaluating the model's performance, while smoothing is more about refining the data signal by reducing noise without addressing the issue of missing values. Encoding, on the other hand, pertains to transforming categorical variables into numerical format and does not directly relate to managing missing data.