Label: Labels are referred to as the final output. The output classes can also be considered as labels. When data scientists speak of labeled data what they mean is a group of samples which have been tagged to one or more labels.
Feature: Features are defined as individual independent variables that act as input. Prediction models use features to make predictions. Moreover, you can also obtain new features from old features using a feature engineering method.
Suppose you want to categorize your friends by their weight and height into different groups like obese, fit, underweight. Here, the inputs are weight and height which acts as a feature and the group names(fit, obese, underweight) which are the final outcome is the label.
Hope this answer helps.
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