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.
Example-
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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