Boruta is by universal reputation dog-slow and not very good. Boruta runs take many hours or days. VIF feature-selection algorithm is not objective, anyway.
You can program your own feature-selection that runs faster. I ran Boruta a few times on various datasets and it wasted 4 days of my time, and the result was inconclusive.
Here's the fast-and-dirty not-100%-scientific approach, culled from consensus in a lot of Kaggle competitions and suchlike. It's 10-1000x faster than Boruta and probably more accurate.
- Run some fast exploratory trees (RF/XGB), i.e. not very deep, not too many trees. Extract feature importances. Repeat for several random seeds and look at average or absmax feature importances.
- For now, throw out very-high-cardinality features (e.g. user-ID, zipcode etc.), since trees won't tend to split on them until very deep and small nodes, so initially exclude them. Let's define "Very-high-cardinality" as >= ~0.3 distinct values per record, or less in a larger dataset.
- Throw out all near-zero features, zero- and very-low-importance features (< 0.02-0.05 f.i. or lower is a rule of thumb).
- Measure the matrix of which features are highly correlated (< 0.4) to each other. Corrplot is a nice way to visualize these, and order correlated features near each other). Pick a sensible color-scale. Identify subgroups of correlated features. Arbitrarily select one feature from those groups (you can revisit this selection later). Or you can decide if this warrants using PCA.
- Rerun, your CV accuracy should have gone way up, and your training time will be way faster (since tree training is quadratic to no. of features). Revisit choices of borderline-low-important or correlated feature, and again use multiple random seeds.
- For exploratory, 5-fold CV is fine, but if training time is an issue, 3-fold works. 10-fold is unnecessary.
- For the subset of remaining features, use a 'respectable' method. See the excellent posts on CrossValidated in feature-selection.
Somebody with a PhD in Stats will probably wag their finger at me for this :)