TreeGrad: Transferring Tree Ensembles to Neural Networks
This work addresses the limitation of offline and greedy tree generation in GBDT implementations for machine learning practitioners, offering a method to enhance flexibility and adaptability.
The paper tackles the problem of converting Gradient Boosting Decision Tree (GBDT) ensembles into neural networks to enable online updates and neural architecture search for split points, achieving minimal performance loss with provided learning bounds.
Gradient Boosting Decision Tree (GBDT) are 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.