# How to incorporate new features in an existing machine learning model?

Suppose we have trained a regression model $$M$$ on a fixed set of $$n$$ features, $$F_1,F_2,…,F_n$$ on a particular dataset $$G$$. Now assume that after model training, additional features ($$F_{n+1},…$$) become available for a subset $$H\subset G$$.

What would be the best way incorporate these features to improve predictions on the subset $$H$$?

I can think of a few possible solutions:

• Train a new model $$N$$ on the dataset $$G$$ where the new features are null in $$G \setminus H$$. This could be useful when $$|H| \ll |G|$$.
• Train a new model $$N$$ on the dataset $$H$$, disregarding the old model and the (useful) training data $$G \setminus H$$.
• Train a submodel $$M'$$, using $$M$$ as a starting point, on the new features ($$F_{n+1},…$$). This has the advantage of not throwing away the useful training done beforehand.

To me the last option seems like the best solution. Unfortunately, I cannot find any literature doing this sort of thing. Does that depend on the type of model? Or is it better to train a new model?