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

## TL;DR

The paper introduces NN-Stacking, a neural network-based method that dynamically adjusts feature weights in linear stacking models, enhancing predictive accuracy by capturing regional variations in model performance.

## Contribution

It generalizes traditional linear stacking by allowing coefficients to vary with input features using neural networks, improving prediction in diverse data regions.

## Key findings

- Improved predictive accuracy over traditional stacking methods.
- Effective in large sample datasets with complex feature interactions.
- Maintains interpretability at a local level.

## Abstract

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 \citet{breiman1996stacked}. 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.