Ground Truth
In the ML context, ground truth denotes factual data that is observed or computed and can be analyzed objectively.
What is Ground Truth?
Ground truth denotes factual data observed or computed and can be analyzed objectively for the specific ML use case.
Ground truth entails a reality check for machine learning outcomes. In ML, ground truthing refers to checking the accuracy of model outcomes against the real world. This term is borrowed from meteorology, which denotes obtaining site information.
Example of Ground Truth
A prediction model is deployed to forecast if target customers will buy a product in the next seven days. The ground truth is available after seven days of model prediction - whether a customer bought a product or not. This delayed ground truth is acquired and compared against the model predictions to assess the predictive performance.
Why is Ground Truth Important?
Ground truth helps ML practitioners to refine their algorithms for enhanced accuracy. Evaluating predictions against ground truth helps ensure the model correctly predicts a phenomenon.
For example, a technique like Bayesian spam filtering where a model is trained to classify spam and non-spam. This training is based on the ground truth of the messages used to train the algorithm. Inaccuracies in the ground truth will propagate inaccuracies in spam/non-spam verdicts by the model.
Supervised ML models learn from the data labels in the training set to predict or classify correctly. The model performance depends on the quality of labeled data, so investing in highly accurate data annotation matters.
Contrarily for unsupervised models, the phrase ground truth does not hold a meaning. The unsupervised ML algorithms look for hidden patterns from raw, unlabeled data.
Once you have ground truth readily available and linked to your prediction event, applying and tracking model performance metrics becomes easy. Capturing ground truth involves these aspects:
- Bias in datasets
- The subjectivity of AI system
- Availability of ground truth
Getting Ground Truth Right
ML algorithms are used to address diverse problems and work in different scenarios. Following are the commonly found conditions that mark the availability of ground truth.
Ground Truth is Available Instantly
An ideal scenario for obtaining ground truth is defined by its immediate availability for each prediction delivered.
E.g., A prediction model deployed to gauge user engagement on the e-commerce platform. After the model predicts, it is immediately evaluated against the ground truth, which is the real-time behavior of e-commerce portal users.
Ground Truth is Delayed
Delayed ground truth defines the most common scenario for getting the ground truth. It becomes available after a specific period, such as a few days or weeks— we have discussed an example of delayed truth in the introductory section.
Ground Truth is Not Available
Not a preferred scenario for ML deployments because it is hard to analyze model performance without ground truth. Sometimes techniques such as proxy metrics and human annotators help in this case.
Ground truthing is simplified with pre-built tools:
- AWS Sagemaker Ground Truth
- Google Cloud - AI Platform Data Labelling Services
- Third-party solutions
ML predictions derived from subjective data and wrong assumptions could be more questionable. Consulting with the right experts will help to establish the ground truth correctly.
Further Reading
What is “Ground Truth” in AI? (A warning.)
Establishing Ground Truth in the Real World
What is Data Labeling and How to Do It Efficiently [Tutorial]