What do you mean by Features and Labels in a Dataset?
Features are individual independent variables which acts as the input in the system. Prediction models uses these features to make predictions. New features can also be extracted from old features using a method known as ‘feature engineering’. To make it simple, you can consider one column of your data set to be one feature. Features are also called attributes. And the number of features is dimensions.
Labels are the final output or target Output. It can also be considered as the output classes. We obtain labels as output when provided with features as input.