Class Labels
In data science, class labels refer to the categories or groups that data points belong to. These labels are used in supervised learning, where the goal is to train a model to predict the class label of new, unseen data points based on their features. Class labels can be binary (e.g. yes/no, true/false) or multi-class (e.g. red/green/blue). The process of assigning class labels to data points is called labeling or annotation. Class labels are important for evaluating the performance of a model, as they allow us to compare the predicted labels to the true labels. The accuracy of a model is typically measured using metrics such as precision, recall, and F1 score, which are based on the number of true positives, false positives, true negatives, and false negatives.
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