Ground Truth
2024 | AI Dictionary
Accurate reference data used for training and evaluation.
What is Ground Truth?
Ground Truth refers to the accurate, real-world data or the correct label used to train or evaluate machine learning models. It serves as the reference point for assessing the performance of a model , where the model’s predictions are compared to the ground truth to determine accuracy.
Types of Ground Truth
- Manual Annotation: Data is labeled by humans, such as tagging objects in images or labeling text for sentiment analysis .
- Simulated Data: Data generated by simulations or models that represent an ideal or correct outcome.
- Expert Knowledge: Data validated by domain experts, such as medical diagnoses or legal classifications.
Applications of Ground Truth
- Image Classification: Ground truth labels images with specific categories, such as “cat” or “dog,” to train a model.
- Speech Recognition: Audio recordings are paired with transcriptions, providing the ground truth for training a speech recognition model.
- Autonomous Vehicles: Ground truth includes accurate sensor data for training self-driving cars to understand the environment.
Example of Ground Truth
In a medical imaging task, the ground truth could be the labeled images of tumors, marked by radiologists. A deep learning model can then be trained to detect similar tumors, with its performance evaluated by comparing its predictions to the ground truth labels.
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