TAG ARCHIVE

approval-prediction

1 MARIA OS blog articles tagged approval-prediction, organized as a Bonginkan topic archive for search engines and LLM retrieval.

1 article|Published by Bonginkan

Judgment OS / Decision Intelligence OS

Core MARIA OS research on turning organizational judgment into executable decision systems.

Agentic Company Architecture

Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.

Responsibility Gates and AI Governance

Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.

Multi-Agent Mathematics

Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.

Evidence, RAG, and Knowledge Governance

Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.

IntelligenceFebruary 14, 202632 min read

Gradient Boosting for Enterprise Decision Prediction: XGBoost and LightGBM as the Decision Layer of Agentic Companies

Why enterprise data is often tabular and how gradient boosting ensembles support approval prediction, risk scoring, and outcome estimation

While deep learning dominates many unstructured tasks, enterprise decision data is frequently tabular: structured features describing decisions, agents, contexts, and outcomes. This paper formalizes gradient boosting (XGBoost/LightGBM) as the Decision Layer (Layer 2) of the agentic company stack, details feature-engineering patterns for enterprise decision tables, and introduces SHAP-based explainability workflows for governance audits. Across evaluated datasets, the approach achieved 91.3% approval-prediction accuracy, 0.94 AUC on risk scoring, and full SHAP traceability integrated with MARIA OS responsibility gates.

gradient-boostingXGBoosttabular-dataapproval-predictionrisk-scoringdecision-predictionensemble-methodsenterprise-AIagentic-companyMARIA OS