märkdata

Märkdata is a term used in Swedish-language data science to refer to labeled data—datasets in which each example is paired with one or more annotations or target labels. Märkdata underpins supervised machine learning by enabling models to learn mappings from inputs to outputs. It encompasses a range of data types, including image datasets with class labels or bounding boxes, text datasets with sentiment or topic labels, audio with transcriptions or labels, and structured tabular data with a designated target column.

Labeling processes are typically performed by human annotators, sometimes assisted by automated or semi-automatic tools. Clear labeling guidelines help ensure consistency, and quality control measures such as spot checks and assessments of inter-annotator agreement are used to monitor reliability. Data provenance and documentation are important for reproducibility and governance.

Quality and bias are central concerns in märkdata. Label quality directly affects model performance and fairness, with common challenges including label noise, class imbalance, annotation bias, and domain shift. Practices such as versioning, transparent schemas, and auditing help mitigate these issues.

Use and evaluation involve splitting märkdata into training, validation, and test sets, and evaluating models with task-appropriate metrics (for example, accuracy, F1 score, or mean squared error). Clear documentation of labeling schemes supports reproducibility and transferability of results.

Relation to other data types: märkdata sits alongside unlabeled data used in unsupervised learning, as well as semi-supervised or weakly supervised approaches that combine labeled and unlabeled signals. The availability and quality of märkdata often determine the success of supervised learning applications across domains.