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We've trained a ML model and deployed it to production. The trained ML model uses about 50-60 features. A user inputs set of information on our platform which is nowhere close to all the features that the model is trained on.

How do we make a prediction with ML algorithm that's trained on far greater number of features than your test point?

A credit scoring example. Model is trained on 1000s of users' credit history, demographics, location, income, expenses, financials etc. Based on this trained model, we'd like to predict the score of a new user on our platform. We can collect some basic info but cannot obtain all the data.

Are there ways to make predictions when your test data point has limited information compared to your trained model? It's also unrealistic to make assumptions about the test data as you simply don't have enough information. What are some other work arounds?

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  • $\begingroup$ I wonder if an autoencoder would "fill in" missing features that could then feed into your credit scoring. $\endgroup$
    – TMBailey
    Nov 23 at 20:12
  • $\begingroup$ you can do missing data imputation. you predict the missing data lets say 30 times (with uncertainty see eg miceforest). then pass those filled in data sets to your model and take average ( and spread of predictions) $\endgroup$
    – seanv507
    Nov 23 at 22:05

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