# The NN-Stacking: Feature weighted linear stacking through neural networks

arXiv: https://arxiv.org/abs/1906.09735 · HTML: https://mlsanity.com/paper/1906.09735/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves](https://mlsanity.com/paper/2108.08723/) (2021)
- [Test-time Collective Prediction](https://mlsanity.com/paper/2106.12012/) (2021)
- [Enhancing Efficiency and Performance in Deepfake Audio Detection through Neuron-level Dropin & Neuroplasticity Mechanisms](https://mlsanity.com/paper/2603.24343/) (2026)

## Abstract (arXiv)

Stacking methods improve the prediction performance of regression models. A simple way to stack base regressions estimators is by combining them linearly, as done by citetbreiman1996stacked. Even though this approach is useful from an interpretative perspective, it often does not lead to high predictive power. We propose the NN-Stacking method (NNS), which generalizes Breiman's method by allowing the linear parameters to vary with input features. This improvement enables NNS to take advantage of the fact that distinct base models often perform better at different regions of the feature space. Our method uses neural networks to estimate the stacking coefficients. We show that while our approach keeps the interpretative features of Breiman's method at a local level, it leads to better predictive power, especially in datasets with large sample sizes.