In this post we are going to discuss categorical features in machine learning and methods to handle these features using two of the most effective methods.
Categorical Features
In machine learning, features can be broadly classified into two main categories:
- Numerical features (age, price, area etc.)
- Categorical features (gender, marital-status, occupation etc.)
All those features that are composed of a certain number of categories are known as categorical features. Categorical features can be classified into two major types:
- Nominal
- Ordinal
**Nominal **features are those having two or more categories, with no specific order. For example, if Gender has two values, male and female, it can be considered as a nominal feature.
Ordinal features on the other hand have categories in a particular order. For example, if we have a feature named Level having values as high, medium and low, it will be considered an ordinal feature, because the order matters here.
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