Evaluating machine learning model with missing features I am working on a credit risk binary classification problem. The classes are GoodPayers and BadPayers.  The training set has variables/features that contains: 


DemoGraphics Data  such as - Age, Education, Loan Amount, Interest Rate
Behavioral Data such as - Payment in Month1, Payment in Month2, Payment in Month3, Payment delay in Month1, Payment delay in Month2. 


The 10-fold cross-validation has 0.82 AUC on this set. 

However, the unseen data just contains the 'Demographics Data' and does not have 'Behavioral Data' of Payment.  How do we deploy/test the model based on DemoGraphics Dataset only?
If less than 20-25% of behavioral data is missing, maybe you could try to impute missing data  using one of the following solutions : 


Impute missing behavioral data using some business rule or by training a machine learning model with demographics data as input and behavioral data as output variable.
Impute missing data with feature mean/median.
Impute missing data by picking up random value in the feature distribution (hot-deck).


In case you have more than 20-25% missing data, it will be really hard to impute values. I think in this situation you should consider creating a new model such as : 


The new model doesn't use behavioral data anymore.
The new model is based on a different train-val-test split in order to have behavioral data in each dataset.


If you can't create a new model neither impute missing data, I guess hot-deck would be the best option you have to avoid bad performance on unseen data.