Regression

Regression is a machine learning task that predicts continuous numeric values — like price, demand, temperature, or risk score — rather than discrete categories. Linear regression is the simplest form, dating back to the early 1800s, but modern regression spans many techniques including ridge and lasso, gradient-boosted trees, neural networks, and Gaussian processes. Real-world examples include predicting house prices on Zillow, forecasting product demand for inventory planning, estimating expected claim amounts in insurance underwriting, and producing risk scores in credit decisions. Mean squared error, mean absolute error, and R-squared are typical evaluation metrics. Regression models underpin financial forecasting, actuarial scoring, and pricing decisions, all of which fall under strict AI governance and AI compliance requirements. Documenting assumptions, error distributions, and group-level accuracy is essential for responsible AI and AI risk management. Regression remains one of the most widely deployed AI terms in enterprise AI.

Centralpoint Brings Regulatory Discipline to Regression: Centralpoint pairs the predictive power of regression models with enterprise-grade governance. Oxcyon's platform is model-neutral — OpenAI, Gemini, Llama, embedded — meters all LLM usage, and keeps your prompts and skills inside your environment. Stand up multiple regression-aware chatbots across your portals with a single JavaScript line.


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