Abstract: The habitability of exoplanets is a hot research topic in the field of the exploration of the universe in recent years.The Machine Learning(ML) technique provides a viable means for classifying exoplanets according to their habita-bility.However,the existing ML-based approaches of habitability classification have some serious shortcomings and li-mitations.To this end,this paper provided a novel method for predicting the habitability of exopla-net based on Gra-dient Boosted Regression Trees(GBRT).First,the physical and astronomical data on the potentially habitable exopla-nets and the inhabitable ones are employed to train by algorithm GBRT.Then,the trained model is used to predict the habitability of the exoplanets in our test set.The simulated experimental results show that the predictive accuracy of the new method is as high as 100%.

Key words: Binary classification, Exoplanet, Gradient boosted regression trees, Habitability