What is Ground Truth in AI?
The accurate, verified, or correct answer that serves as the benchmark for training and evaluating AI models.
Definition
Ground Truth in AI refers to the accurate, verified, or objectively correct information that serves as the reference standard for training machine learning models and evaluating their performance against known correct answers.
Purpose
Ground truth provides the foundation for supervised learning by giving AI systems correct examples to learn from, and serves as the benchmark for measuring how well models perform on tasks.
Function
Ground truth works by providing labeled datasets where the correct answers are known and verified, allowing models to learn patterns and enabling objective evaluation of predictions against these established correct values.
Example
In medical imaging AI, ground truth might be expert radiologist diagnoses verified by biopsy results, used to train models to detect cancer and measure their diagnostic accuracy.
Related
Connected to Training Data, Supervised Learning, Model Evaluation, Data Labeling, Accuracy Metrics, and Quality Assurance processes.
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