Probabilistic Deep Forest
Novel implementation of the "Probabilistic Deep Forest (PDF)", a model that enhances Deep Forest to handle noisy, real-world data. It solves the critical issue of uncertainty loss during layer-by-layer prediction by using Probabilistic Random Forests as its core estimators.
This project presents a novel model, resulting from the combination of a Deep Forest (DF) and a Probabilistic Random Forest (PRF). Standard machine learning models, including DF, typically assume that input data is precise. However, real-world data, especially from scientific domains like astronomy, is inherently noisy. This work modifies the Deep Forest architecture to use Probabilistic Random Forests (PRF) as its base estimators, creating a Probabilistic Deep Forest (PDRF) that can leverage data uncertainty to make more robust and accurate predictions.
The core of this project is a custom-built Python library, probabilistic-deep-forest, which is a modified version of the original deep-forest library, along with a custom PRF4DF estimator compatible with the scikit-learn ecosystem.
The GitHub repository is not currently available.
Collaborators
Fabio Vicig