Ground Truth
The accurate, verified labels or outcomes used to train and evaluate machine learning models.
In-Depth Explanation
Ground truth refers to the known correct answers used to train and evaluate machine learning models. It serves as the benchmark against which model predictions are measured.
Ground truth sources:
- Human annotation: Expert labeling
- Historical outcomes: Past real results
- Verified records: Authoritative data
- Consensus: Multiple annotator agreement
- Physical measurement: Sensor data
Quality characteristics:
- Accuracy (actually correct)
- Consistency (same standards throughout)
- Completeness (covers needed cases)
- Representativeness (reflects real distribution)
Challenges:
- Obtaining reliable labels
- Subjective judgements
- Changing truth over time
- Cost of verification
- Edge cases and ambiguity
Business Context
Ground truth quality directly determines model quality. Investing in accurate ground truth is one of the highest-ROI activities in ML.
How Clever Ops Uses This
We help Australian businesses establish reliable ground truth processes, ensuring AI models are trained and evaluated against accurate standards.
Example Use Case
"Using verified fraud investigation outcomes as ground truth to train and evaluate a fraud detection model, tracking prediction accuracy against known fraud cases."