In supervised learning, what does labeled data refer to?
Data with both feature values and corresponding output labels
Explanation:
In supervised learning, labeled data means each training example contains:
Input features (the data used for prediction), and
Output labels (the correct answer or target value the model learns to predict).
Example:
Feature values: Email text, sender, subject
Label: “Spam” or “Not Spam”
The model learns the relationship between the features and labels so it can make predictions on new, unseen data.
