Märgistamisvigu

Märgistamisvigu refers to errors that can occur during the process of labeling data, particularly in machine learning and artificial intelligence contexts. These errors can arise from various sources, including human mistakes by annotators, inconsistencies in labeling guidelines, ambiguity in the data itself, or issues with the annotation tools used. When data is mislabeled, it can negatively impact the performance of machine learning models that are trained on this data. For example, if images of cats are incorrectly labeled as dogs, a model trained on this dataset might learn to identify dogs as cats.

The consequences of märgistamisvigu can range from reduced model accuracy to biased outputs, depending on the nature and prevalence of the errors. Detecting and correcting these mistakes is a crucial step in the data preparation pipeline. Techniques for identifying märgistamisvigu include inter-annotator agreement checks, where multiple annotators label the same data and discrepancies are flagged, and outlier detection methods applied to the labels. Post-training analysis of model errors can also help pinpoint areas where mislabeled data might be causing problems. The goal is to ensure the integrity and quality of the training data to build robust and reliable AI systems.