Features roles and types¶
A feature’s role determines how it’s used during machine learning.
- Reject means that the feature is not used
- Input means that the feature is used to build a model, either as a potential predictor for a target) or for clustering
- Use for display only means that the feature is not used to build a model, but is used to label model output. This role is currently only used by cluster models.
A feature’s variable type determines the feature handling options during machine learning.
- Categorical variables take one of an enumerated list values. The goal of categorical feature handling is to encode the values of a categorical variable so that they can be treated as numeric.
- Numerical variables take values that can be added, subtracted, multiplied, and so on. There are times when it may be useful to treat a numerical variable with a limited number of values as categorical.
- Text variables are arbitrary blocks of text. If a text variable takes a limited number of values, it may be useful to treat it as categorical.