In supervised machine learning, a testing dataset has class labels for each observation:

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BlackTom AI · Accepted Answer

The question asks about the labeling of a testing dataset in supervised machine learning.
Option 1 (True): In supervised learning, the testing dataset is used to evaluate model performance by comparing the model's predicted labels to the true (ground-truth) labels. Therefore, each observation in the test set has a known class label so that metrics like accuracy, precision, recall, and F1-score can be computed.
Option 2 (False): This would imply that test data lacks ground-truth labels. While there are learning paradigms where labels are not required for training (unsupervised) or where labeling is scarce (semi-supervised or active learning scenarios), the standard supervised evaluation framework relies on labeled test data to measure how well the model generalizes. If test labels were unavailable, we could not quantify prediction quality using conventional supervised metrics.

In supervised machine learning, a testing dataset has class labels for each observation:True/False

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