I have a model generated using an imputed data set with imputation accuracy of 75%.
If the model using imputed data has an accuracy of 80%
What would be the community consensus to report the accuracy of such models with imputed data?
I assume the 80% accuracy has an error-rate of 25% because of the imputation error? Is there any consensus to report such results by taking into consideration of the imputation accuracy and model accuracy? Any methods available to estimate the consensus errors of the imputation and predictive model?
A bit of background: I work in healthcare data science; data is often noisy, missing or unavailable to compute using a complete matrix. Many of us use imputation methods to augment this; however for various projects that I worked non-imputed models perform better in terms of both accuracy and model quality. Also, clinical intuition suggests that a missing value indicates the real-world scenario where a test is specialized that imputing the test values using statistical estimates may ultimately lead to unwarranted clinical implications.