Detecting Statistical Interactions from Neural Network Weights
This addresses the challenge of model interpretability for machine learning practitioners, though it appears incremental as it builds on existing interaction detection approaches.
The paper tackles the problem of interpreting neural networks by developing a framework to detect statistical interactions from learned weights, achieving significantly better or similar performance compared to state-of-the-art methods without exponential search.
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can achieve significantly better or similar interaction detection performance compared to the state-of-the-art without searching an exponential solution space of possible interactions. We obtain this accuracy and efficiency by observing that interactions between input features are created by the non-additive effect of nonlinear activation functions, and that interacting paths are encoded in weight matrices. We demonstrate the performance of our method and the importance of discovered interactions via experimental results on both synthetic datasets and real-world application datasets.