Can we look just at the other features when we have a missing vaue? For my classification task i have 3 features a,b,c.
Feature c is missing for some datas.
I can have already a good score for my classifier training with the two other features a,b and even better if I don't look at the missing data and I train with a,b,c.

Is it possible to train my classifier with features a,b,c on all the data which don't have missing values, and in my prediction phase, if the feature c is missing, to predict the class like if I had train my classifier with a and b?
Try a few options and pick the best.


Fill in the mean/median from known $c$'s in the $a,b,c$ model
Create a linear regression model to predict $c$ from $a,b$, fill the estimate $c$ from this model in the $a,b,c$ model
Train both models and use the applicable one


Personally, I think 2. will work best in most cases, but 1. can already be sufficient when $c$ is not correlated to $a$ or $b$.