labelingstaak
Labelingstaak is a term used to describe the process of assigning predefined labels to data items in a dataset for supervised machine learning. It covers the collection, organization, and verification of labeled data and is a core step in creating training, validation, and test sets. Labelingstaak applies across data modalities, including text, images, audio, video, and sensor data, and may involve single-label or multi-label schemes.
The task is typically performed by human annotators or crowdsourced workers, guided by formal annotation guidelines.
Design considerations include selecting a label schema, defining granularity, handling ambiguity, and ensuring privacy and consent
Evaluation and use: Labeled data is used to train supervised models; the quality of labels directly affects
Labelingstaak faces challenges such as subjectivity, context dependence, annotation drift over time, and biases. Best practices