On Incorporating new Variables during Evaluation

Harsimran Bhasin, Soumyadeep Ghosh

Keywords: classification, model inferencing, evaluation, tabular data

Abstract:

Any classification or regression model needs access to the same features and input
that were utilized to train the model. However in real world scenarios, several models are in operation for years and new variables/features may be available
during the inferencing stage. If such features are to be utilized, their values have to be captured in the dataset that was utilized for training the model. We propose
a model agnostic approach where we trained a model without the access to those
features during the training stage, which could benefit from the additional features
available during testing. We show that by using the proposed approach and without
any access to the extra features during the training phase, we are able to improve
the performance of the model on four real world tabular datasets. We provide
extensive analysis on how and which variables result in the improvement over the
model which was trained without the extra feature(s).

Submission Number: 49