# Can I use more features for my training data than my test data will supply?

I am pretty new to the data science game so pardon me, if the answer to my question should be a no-brainer.

We are looking at manufacturing / quality data where products are labeled 'okay' or 'not okay' based on different measurements. In our data set, we also have information on the product type, which machine was used to build it, at what time it was build, temperature, etc.

We are trying to train a classification model that will correctly classify 'okay' or 'not okay' products. At the point where the classification should be done, we do not have the measurements of the product, but all the other data I mentioned. Someone suggested using all data to train the model incl. the measurements, even though the test data will not have this information.

So the question is: is it feasible and does it make sense to train a model with more features than it can test on the test data? What ever the answer - please explain because I just can't seem to wrap my head around it.

Kind regards

Julia

Features should be same for training and test/prediction sets. You should not use (actually can't for most packages) n features for training, n-k for test/prediction etc.
If the column you mentioned have NaNs for not all observations, you can try models that robust to null values. (ie: xgboost)