Suppose we have an input feature that is highly predictive of the outcome we want to predict. However, the feature is missing on 99% of the samples in the data set. What is the best way to use this feature in building a prediction model? Any ideas would be greatly appreciated!
Its best to remove such a variable. Reasons are following:
- Artificial imputation can add bias and result cannot be justified because 99% data for the particular variable was artificially created.
- The variables/features that you choose for building the predictive model should have low correlation with the target/outcome variable/feature. Because, variable that are highly correlated with the target/outcome variable reduces the model predictive performance.
- You want a model which has low bias and low variance.