Gradient Boosted Decision Tree Neural Network
This provides a more interpretable and efficient alternative to complex ensemble methods for machine learning practitioners.
The paper tackles the problem of approximating gradient boosted decision trees with a simpler neural network architecture, resulting in a model called Hammock that achieves performance similar to GBDTs on various datasets.
In this paper we propose a method to build a neural network that is similar to an ensemble of decision trees. We first illustrate how to convert a learned ensemble of decision trees to a single neural network with one hidden layer and an input transformation. We then relax some properties of this network such as thresholds and activation functions to train an approximately equivalent decision tree ensemble. The final model, Hammock, is surprisingly simple: a fully connected two layers neural network where the input is quantized and one-hot encoded. Experiments on large and small datasets show this simple method can achieve performance similar to that of Gradient Boosted Decision Trees.