Ground Truth
Ground truth refers to the correct, verified labels or annotations in a dataset used to train and evaluate machine learning models. The quality of ground truth directly impacts model reliability.
Understanding Ground Truth
Ground truth refers to the verified, accurate data or labels that serve as the authoritative reference for training and evaluating machine learning models. In supervised learning, ground truth labels define the correct output for each input example—whether that means the correct category for an image, the precise bounding box in object tracking, or the ideal summary in a summarization task. Establishing high-quality ground truth typically requires human annotation, which can be expensive and time-consuming, making human-in-the-loop processes essential for maintaining label accuracy. The reliability of ground truth directly impacts every downstream metric, from benchmark scores to real-world model performance. Poor or noisy ground truth can lead to models that learn incorrect patterns, which is why data quality assurance, inter-annotator agreement measurement, and careful imputation of missing values are foundational practices in any serious AI project.
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Data Science
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A/B Testing
A/B testing is an experimental method that compares two versions of a model, prompt, or interface to determine which performs better. In AI, A/B testing helps evaluate model outputs, UI changes, and prompt strategies by measuring user engagement or accuracy.
Annotation
Annotation is the process of adding labels or metadata to raw data to create training datasets for supervised learning. Data annotation can involve labeling images, tagging text, or marking audio segments.
Benchmark
A benchmark is a standardized test or dataset used to evaluate and compare the performance of different AI models. Common benchmarks include MMLU, HumanEval, and ImageNet.
Causal Inference
Causal inference is the process of determining cause-and-effect relationships from data, going beyond mere correlation. AI systems increasingly use causal reasoning to make more robust and interpretable decisions.
Cross-Validation
Cross-validation is a model evaluation technique that splits data into multiple folds, training and testing on different subsets in rotation. K-fold cross-validation provides more reliable performance estimates than a single train-test split.
Data Augmentation
Data augmentation is a technique that artificially increases training dataset size by creating modified versions of existing data. In computer vision, this includes rotations, flips, and color changes; in NLP, it includes paraphrasing and synonym replacement.
Data Drift
Data drift occurs when the statistical properties of production data change over time compared to the training data. Drift can degrade model performance and requires monitoring and retraining strategies to address.
Data Labeling
Data labeling is the process of assigning meaningful tags or annotations to raw data to create supervised learning datasets. High-quality labeled data is essential for training accurate machine learning models.