What is meant by conflict in labeling for a classification machine learning problem?

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Conflict in labeling occurs when there is disagreement or ambiguity about the true class label of a data point in a classification machine learning problem. It can arise due to various reasons such as noise in the data, inherent subjectivity or variability in the labeling process, or when multiple labels can be valid for a particular example. Conflict in labeling can result in reduced accuracy of the classifier if the labeling error rate is high or if the classifier is not equipped to handle such conflicts. Therefore, it is important to carefully examine and preprocess the data to reduce the incidence of conflicts in labeling.