SmallML: Bayesian Transfer Learning for Small-Data Predictive Analytics

• URL: http://arxiv.org/abs/2511.14049v1
• Date: Tue, 18 Nov 2025 02:00:55 GMT
• Title: SmallML: Bayesian Transfer Learning for Small-Data Predictive Analytics
• Authors: Semen Leontev,
• Abstract summary: SmallML achieves enterprise-level prediction accuracy with datasets as small as 50-200 observations.<n> validation on customer churn data demonstrates 96.7% +/- 4.2% AUC with 100 observations per business.<n>By enabling enterprise-grade predictions for 33 million U.S. SMEs, SmallML addresses a critical gap in AI democratization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Small and medium-sized enterprises (SMEs) represent 99.9% of U.S. businesses yet remain systematically excluded from AI due to a mismatch between their operational scale and modern machine learning's data requirements. This paper introduces SmallML, a Bayesian transfer learning framework achieving enterprise-level prediction accuracy with datasets as small as 50-200 observations. We develop a three-layer architecture integrating transfer learning, hierarchical Bayesian modeling, and conformal prediction. Layer 1 extracts informative priors from 22,673 public records using a SHAP-based procedure transferring knowledge from gradient boosting to logistic regression. Layer 2 implements hierarchical pooling across J=5-50 SMEs with adaptive shrinkage, balancing population patterns with entity-specific characteristics. Layer 3 provides conformal sets with finite-sample coverage guarantees P(y in C(x)) >= 1-alpha for distribution-free uncertainty quantification. Validation on customer churn data demonstrates 96.7% +/- 4.2% AUC with 100 observations per business -- a +24.2 point improvement over independent logistic regression (72.5% +/- 8.1%), with p < 0.000001. Conformal prediction achieves 92% empirical coverage at 90% target. Training completes in 33 minutes on standard CPU hardware. By enabling enterprise-grade predictions for 33 million U.S. SMEs previously excluded from machine learning, SmallML addresses a critical gap in AI democratization. Keywords: Bayesian transfer learning, hierarchical models, conformal prediction, small-data analytics, SME machine learning

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