Feature vs label in Machine Learning ?

 In the context of machine learning and data analysis, "features" and "labels" are two important concepts.

Features refer to the input variables or attributes that are used to represent the data. These are the characteristics or properties of the data that are considered as inputs to a machine learning model. For example, if you're building a spam detection system, the features could include the subject line, sender, and body of an email.

Labels, on the other hand, refer to the output variable or the target variable that you want the machine learning model to predict or classify. The labels represent the desired outcome or the ground truth associated with each data point. In the spam detection example, the labels would indicate whether an email is spam or not.

To train a machine learning model, you need a labeled dataset where each data point has both the features and the corresponding labels. The model learns patterns and relationships between the features and labels during the training process and uses that knowledge to make predictions or classifications on new, unseen data.

In summary, features are the input variables that describe the data, while labels are the output variables that represent the desired outcome or prediction associated with the data.