Problem with adaboost

For some reason, boosting doesn't seem to work. I don't think the issue here is the same as #42. I tried the example from Elements of Statistical Learning and compared to fastAdaboost in R

julia> using Distributions, DecisionTree, RCall, DataFrames

julia> # Boosting example from EoSL
       X = randn(1000, 10);

julia> y = Vector{Int64}(vec(sum(abs2, X, 2) .> quantile(Chisq(10), 0.5)));

julia> # Use DecisionTree
       ada1 = DecisionTree.build_adaboost_stumps(y, X, 5);

julia> mean(apply_adaboost_stumps(ada1..., X) .== y)
0.579

julia> # Use fastAdaboost
       R"library(fastAdaboost)";

julia> df = DataFrame(X);

julia> df[:y] = y;

julia> ada2 = R"adaboost(y ~ x1 + x2 + x3 + x4 + x5 + x6 +x7 + x8 + x9 + x10, data = $df, 5)";

julia> rcopy(R"predict($ada2, newdata = $df)$error")
0.021

Furthermore, the build_adaboost_stumpss is much slower than adaboost from fastAdaboost. It looks like build_adaboost_stumps might not use the same optimizations as build_tree.

Start by reproducing the Julia example using build_adaboost_stumps and apply_adaboost_stumps, then compare its accuracy and runtime with R's fastAdaboost. Inspect the boosting entry points and the related build_tree path mentioned in the issue. Done means the boosting result and performance gap are explained and the reported behavior is corrected or documented.