Hi,
You have to see the scenario in bias / variance settings. Typically single decision tree has less bias and high variance. On the other hand, all the advanced methods like random forest (bagged decision trees), gradient boosting machines etc were typically introduced to reduce the variance. One should not look only from the misclassification rate perspective but also from the generalization ability of the model.
To improve the gradient boosting machine results, you will have to play with multiple algorithm parameters, like number of iterations, shrinkage (or learning parameter), training proportion, leaf size etc. If you decrease shrinkage parameter, don't forget to increase the number of iterations.
Best,
abhijit
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