RFX-Fuse: Breiman and Cutler's Unified ML Engine + Native Explainable Similarity

TL;DR

RFX-Fuse unifies multiple machine learning capabilities of Breiman and Cutler's original Random Forest into a single, GPU/CPU-supported engine, offering native explainability, similarity measures, and data imputation validation in one model.

Contribution

It introduces RFX-Fuse, a unified ML engine that consolidates prediction, similarity, explanation, outlier detection, and imputation into one model, simplifying modern ML pipelines.

Findings

Native explainable similarity via proximity importance.

Effective dataset-specific imputation validation without ground truth.

Single model approach reduces complexity of ML workflows.

Abstract

Breiman and Cutler's original Random Forest was designed as a unified ML engine -- not merely an ensemble predictor. Their implementation included classification, regression, unsupervised learning, proximity-based similarity, outlier detection, missing value imputation, and visualization -- capabilities that modern libraries like scikit-learn never implemented. RFX-Fuse (Random Forests X [X=compression] -- Forest Unified Learning and Similarity Engine) delivers Breiman and Cutler's complete vision with native GPU/CPU support. Modern ML pipelines require 5+ separate tools -- XGBoost for prediction, FAISS for similarity, SHAP for explanations, Isolation Forest for outliers, custom code for importance. RFX-Fuse provides a 1 to 2 model object alternative -- a single set of trees grown once. Novel Contributions: (1) Proximity Importance -- native explainable similarity: proximity measures that samples are similar; proximity importance explains why. (2) Dataset-specific imputation validation for general tabular data -- ranking imputation methods by how real the imputed data looks, without ground truth labels.