# GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions

@article{Yang2020GAMINetAE, title={GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions}, author={Zebin Yang and Aijun Zhang and A. Sudjianto}, journal={ArXiv}, year={2020}, volume={abs/2003.07132} }

The lack of interpretability is an inevitable problem when using neural network models in real applications. In this paper, a new explainable neural network called GAMI-Net, based on generalized additive models with structured interactions, is proposed to pursue a good balance between prediction accuracy and model interpretability. The GAMI-Net is a disentangled feedforward network with multiple additive subnetworks, where each subnetwork is designed for capturing either one main effect or one… CONTINUE READING

#### Supplemental Code

GITHUB REPO

Via Papers with Code

Generalized additive model with pairwise interactions

3 Citations

Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines

- Computer Science, Mathematics
- 2020

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 38 REFERENCES

Enhancing Explainability of Neural Networks through Architecture Constraints

- Medicine, Mathematics
- 2020

- 6
- PDF

From local explanations to global understanding with explainable AI for trees

- Medicine, Computer Science
- 2020

- 105

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

- Mathematics, Computer Science
- 2016

- 3,234
- PDF

Detecting Statistical Interactions from Neural Network Weights

- Mathematics, Computer Science
- 2018

- 60
- PDF