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

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.