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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?

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That is commonly called incremental or online machine learning.

Whether to train a new model or augment an existing model is an empirical question. It is often a function of model size (augmenting uses less computational resource) and the value of new data (augmenting increases the weight of new data, whereas retraining weights all data equally).

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