# Feature Types

**Discrete**

**Numbers****Categorical****Categorical data are variables that contain label values rather than numeric values.**

**The number of possible values is often limited to a fixed set.**

**labels, usually discrete values such as gender, country of origin, marital status, high-school graduate**

**Continuous (the opposite of discrete): real-number values, measured on a continuous scale: height, weight.**

**In order to compute a regression, categorical predictors must be re-expressed as numeric: some form of indicator variables (0/1) with a separate indicator for each level of the factor.**

**Discrete with many values are often treated as continuous, i.e. zone numbers - > binary**