Allow mtry to accept a proportion of features, maybe via an mtry_prop parameter?

[stevenpawley]

Machine learning packages such as scikit-learn and mlr3 allow mtry/max_features type hyperparameters to be supplied (and tuned) as a proportion of available features ('max_features' in sklearn and 'mtry_ratio' in mlr3).

Currently, parsnip appears to require the number of features to be supplied as an integer for tree-based models, and with mtry needing to be finalized if this is unknown. Maybe I have missed something, but this appears problematic within a workflow if preceeding recipe steps are adding or selecting features because the total number of features available are unknown. I couldn't see a way of ensuring a proper range of mtry when tuning hyperparameters as part of a workflow that contains such recipe steps? Currently, the only option appears to be to set the upper range of mtry to be as large as the maximum possible number of features and allowing tune/ranger to provide the warning that mtry has exceeded the number of features available?

Although most random forest implementations in R seem to only allow mtry as a number of features rather than proportion, mlr3 appears to catch this in its fit method via the mtry_ratio hyperparameter. Would this be possible in parsnip?

[juliasilge]

For xgboost, we did set up colsample_bynode (i.e. mtry) and colsample_bytree to be specified as either counts = FALSE or counts = TRUE so you can do this for xgboost FWIW.

We'd have to think about what it would take to support this more broadly.

[stevenpawley]

Thanks for the information re. XGBoost, very useful. FYI, ranger's mtry also accepts a function that returns the number of variables:

library(ranger)

ranger(Species ~ ., data = iris, mtry = function(x) ceiling(x * 0.3))

[simonpcouch]

Our docs will be clarified here on merge of #734. :)

[jxu]

There is no need for a counts flag right? If mtry is between [0,1], use as proportion, if it's an integer > 1, use as count, otherwise error? As long as it's documented it shouldn't be too confusing.

[simonpcouch]

mtry = 1 is ambiguous, unfortunately.🙂

[jxu]

if you mean count = 1, I think only decision stumps will be learned? This is technically possible but not meaningful.

[simonpcouch]

I mean mtry. If mtry = 1, it could be interpreted as a proportion, in which case all predictors are sampled, or it could be interpreted as a count, in which case 1 predictor is sampled.

[jxu]

Ok. I mean if you exclude the count interpretation for mtry = 1, then you can support both. But I guess a 1 predictor sampling option is desired.