A feature value is a data value. In programming languages, a feature is represented as a primitive data type, such as an int, string, array, or boolean. Features are ultimately transformed into a numerical input format when they are used by ML models for training or inference.
Data types for features are important because they determine how the feature values can be encoded and represented in a machine learning model. Choosing the appropriate data type for each feature can impact the accuracy and performance of the model. For example, if you have a numerical feature that a downstream model may want to normalize, you probably should use a float or double data type instead of an int data type as the normalized numerical feature values will lie in the range 0 to 1.