boost_from_prediction.jl

using XGBoost

dtrain = DMatrix("../data/agaricus.txt.train")
dtest = DMatrix("../data/agaricus.txt.test")
watchlist  = [(dtest,"eval"), (dtrain,"train")]


###
# advanced: start from a initial base prediction
##

print ("start running example to start from a initial prediction\n")
param = ["max_depth"=>2, "eta"=>1, "silent"=>1, "objective"=>"binary:logistic"]

bst = xgboost(dtrain, 1, param=param, watchlist=watchlist)
# Note: we need the margin value instead of transformed prediction in set_base_margin
# do predict with output_margin=True, will always give you margin values before logistic transformation

ptrain = predict(bst, dtrain, output_margin=true)
ptest  = predict(bst, dtest, output_margin=true)

# set the base_margin property of dtrain and dtest
# base margin is the base prediction we will boost from

set_info(dtrain, "base_margin", ptrain)
set_info(dtest, "base_margin", ptest)

watchlist  = [(dtest,"eval2"), (dtrain,"train2")]
print ("this is result of running from initial prediction\n")

bst = xgboost(dtrain, 1, param=param, watchlist=watchlist)