scikit learn - how to produce similar results using gradient boosting tree models from Spark ML and from sklearn -


when tested gradientboostingclassifier from sklearn on binary classification problem, after tuning parameters, auc can reach 0.89 on average on test data set. while using same data , doing same classification problem except gradient boosted tree model spark ml used; no matter how change parameters, auc can never reach above 0.80.

i understand gradient boosting tree model spark ml , sklearn have no reason give similar results, if possible, can shed light on problem?

ps: data preprocessing both models identical, , sklearn model, used # of iteration=200, learning rate =0.05, max_depth=4.


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