I have successfully built a logistic regression prediction model based on data set that is complete and clean, i.e., there is no missing values and the data is consistent.Now, for deploying the model and testing it for online use, there is missing values in the inputs, i.e., not all inputs are available to predict the target value.Is there a standard way to deal with this?
Deploying the prediction model under missing values for test data
machine learning;predictive modeling;missing data
I can think of three ways to deal with the problem:Treat missing value as another feature: Imagine you have a feature like date of graduation. One possible (likely?) reason why this value is missing might be that the person did not graduate. So you could build a model which as a binary feature graduation date is available and the actual graduation date as another feature.Predict the missing values: If data is missing because of your lack of knowledge of it (in contrast to the first point), then you might think about trying to predict the missing value. You could  also add a feature which encodes the certainty of the predicted value being correct.Skip the feature: If it is missing very often and if it doesn't add much value to your prediction, you might simply want to remove it.