Why regression used in apply_forest only if type of labels in model is Float64?

Hi, I found that for regression algorithm in apply_forest function (mean) the labels type T of model should be exact Float64:

function apply_forest(forest::Ensemble{S, T}, features::AbstractVector{S}) where {S, T}
    n_trees = length(forest)
    votes = Array{T}(undef, n_trees)
    for i in 1:n_trees
        votes[i] = apply_tree(forest.trees[i], features)
    end
    if T <: Float64
        return mean(votes)
    else
        return majority_vote(votes)
    end
end

Is there any particular reason why condition is not T <: AbstractFloat? Also, the documentation noted that regression choosed when labels/targets of type Float, not Float64.
Thanks!

Start by locating the apply_forest function shown in the issue and inspect how label types select regression versus classification. Compare the Float64 condition with the documentation's claim about Float labels; done means the implementation and documentation consistently describe and handle the supported regression label types.