[ScoutOrgo]

I think it is actually preferable to start by converting categorical variables to numeric most of the time, even if they are not ordinal. The RF algo can separate off individual classes with 2 splits (e.g. <=7 then >=7) if a single class is very important. The "pool" of features for RF sampling also doesn't get diluted with one hot encoded classes from the one feature.

I am pretty sure I've seen this done successfully in kaggle a bunch before, but don't have any sources on hand for evidence that this method is "better". It does however make it much easier to just throw the data into the RF and check the feature importances to see which features are helping the most.

[eden_h]

The only case it struggles with is when the grouping is difficult to achieve in a small amount of splits, such as 1,3,5 against 2,4,6,7, especially when each split will need to show more predictive capability against any of the other column options.