Deep neural networks often suffer from limited interpretability despite their high predictive performance. This work presents a methodology for training Generalized Additive Models using deep neural-network architectures to obtain accurate and explainable machine-learning systems.
The proposed approach independently trains neural networks to estimate feature effects while preserving the additive structure required for transparency and interpretability. The presentation discusses theoretical aspects, optimization strategies, and applications in explainable AI and cybersecurity.