Abstract: As a typical "white box" recognition method, the traditional Decision Tree (DT), which is easy to be interpreted and trained, has been widely applied in the Partial Discharge (PD) pattern recognition of high voltage cables. However, the disadvantages of the DT method are significant, including the limited recognition accuracy, poor anti-interference ability and poor generalization ability, etc. In order to overcome the challenge, Gradient Boosted Decision Tree (GBDT) and Random Forest (RF) based PD pattern recognition methods are presented in the paper. Firstly, 3500 PD samples are obtained based on the PD testing of five types of artificial defects of ethylene-propylene (EPR) cables in the high voltage lab. Secondly, PD data pre-processing is carried out and 35 types of PD features are extracted from the raw data. Thirdly, the principles of GBDT and RF based PD pattern recognition are presented in details. Finally, the 3500 PD samples are applied to evaluate the GBDT and RF based PD pattern recognition methods, which are compared with the traditional DT method. The results show that the GBDT based PD pattern recognition is with the highest accuracy, which is 92.5%, and the RF based PD pattern recognition is with the highest recognition efficiency. GBDT and RF based PD pattern recognition methods show remarkable advantage, compared with the DT method, which are both applicable for industrial application of PD based condition monitoring of high voltage cables.