Evaluation
Ground Truth
The correct, verified answer that you compare your AI's predictions against, the gold standard for measuring accuracy.
Definition
The known, validated correct labels or values in a dataset used to evaluate model performance. Serves as the benchmark for measuring prediction accuracy.
Why it matters
Without reliable ground truth, you can't tell if your model is getting better or worse, measurement requires a standard.
Where Sophizo applies this
Sophizo deploys Ground Truth inside revenue and AI engagements with growth-stage operators and PE-backed portfolios.
See ForecastIQ →Related terms in Evaluation
Agent Evals
Standardized tests for AI agents to prove they are smart, safe, and reliable before they are deployed.
AI Model Monitoring
Keeping a constant watch on a deployed AI to make sure it hasn't gotten broken or less accurate over time.
Area Under the Curve (AUC)
A score from 0 to 1 that tells you how good your model is at distinguishing between two things (like spam vs. not spam).
Conformal Prediction
A technique that tells you not just what the AI predicts, but how confident it is, with a mathematical guarantee.
From vocabulary to outcomes
Ready to put Ground Truth to work?
Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.
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