multiviewstacking: A Python Package for Training Multi-View Stacking Classifiers

The multiviewstacking Python package provides an open-source implementation of the Multi-View Stacking (MVS) algorithm for supervised classification tasks. MVS extends traditional stacked generalization by training separate first-level learners on multiple complementary views of the same data and combining their predictions through a meta-learner. The package is designed with flexibility and interoperability in mind, supporting any combination of scikit-learn or custom models. It includes built-in data validation, quality-control tests, and an example multi-view dataset for quick experimentation. multiviewstacking enables researchers to easily build and evaluate heterogeneous multi-view classifiers, facilitating data fusion and ensemble learning applications across diverse domains, such as activity recognition, cybersecurity, and biomedical data analysis. The code and examples of the package can be found at: https://github.com/enriquegit/multiviewstacking.