Chapter 08 · Finance, data & evidenceData, Technology & Verification Systems
Machine learning model
Definition
A machine learning model is a computational model trained on data to identify patterns, make predictions or classify information.
References
This reference provides supporting context for how “Machine learning model” is defined and used.
Overview
What it means in practice
Machine learning model should be read as a data and verification term. Its meaning depends on the system boundary, data source, method, controls and the decision the information is meant to support.
In practice, users should explain what is measured or represented, where the data comes from, how it is transformed and what limitations remain. That keeps machine learning model useful without overstating precision, automation or assurance.
Why it matters
Machine learning model matters because sustainability decisions often depend on data that moves between teams, systems, suppliers and assurance processes. Clear wording helps readers distinguish evidence, estimates, system design and interpretation.
Common misconception
A common error is to treat Machine learning model as proof of accuracy by itself. The term may describe a tool, structure or method, but reliability still depends on data quality, governance, controls and context.
Review questions
What source, method and control environment sit behind the data? What does the term prove, and what does it not prove? Can another reviewer trace the same conclusion from the available records?
How it is used
The term appears in capital allocation, risk assessment, measurement, valuation, due diligence and performance analysis, where investors, lenders, analysts, data providers and sustainability teams use it to classify, assess or communicate a computational model trained on data to identify patterns, make predictions or classify information.
Its correct use depends on the calculation method, data provenance, assumptions, boundary and decision purpose.