Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn. Many implementations can only produce trees in an offline manner and in a greedy manner. We explore ways to convert existing GBDT implementations to known neural network architectures with minimal performance loss in order to allow decision splits to be updated in an online manner and provide extensions to allow splits points to be altered as a neural architecture search problem. We provide learning bounds for our neural network and demonstrate that our non-greedy approach has comparable performance to state-of-the-art offline, greedy tree boosting models.
展开▼
