In the realm of machine learning, features denote the input variables or attributes employed for training a model. These features serve to represent the characteristics or properties of the data under analysis and are utilized by the model to generate predictions or perform classifications.
Features can be classified into two main types: numerical and categorical. Numerical features represent quantitative values, like age or temperature. On the other hand, categorical features represent attributes that can assume a restricted set of values, such as color or category.