How to get reproducible results of classification models?: example using DecisionTree


I would like to train a classifier (eg. Random Forests) and I would like to get the same results if I train/run the model again. My first attempt was to try to set the seed of the random number generator, like this:

# example from
using DecisionTree
features, labels = load_data("iris")
features = float.(features)
labels = string.(labels)

# train random forest classifier
# using 2 random features, 10 trees, 0.5 portion of samples per tree, and a maximum tree depth of 6

Random.seed!(1234) # My attempt here!

model = build_forest(labels, features, 2, 10, 0.5, 6)

n_folds=3; n_subfeatures=2
accuracy = nfoldCV_forest(labels, features, n_folds, n_subfeatures)

Unfortunately, the resulting model seems to be a bit different each time I run the code. The same for the accuracy. I am using DecisionTree v0.10.1

Please, how could I get reproducible results?

Thank you in advance