Categorical Class Labels
In data science, categorical class labels refer to a type of data that represents a set of possible categories or classes that an observation can belong to. These labels are used in supervised learning algorithms to train models to predict the class of new observations based on their features. Categorical class labels can be nominal, where the categories have no inherent order, or ordinal, where the categories have a natural ordering. Examples of nominal categorical class labels include colors, types of animals, or political affiliations, while examples of ordinal categorical class labels include education levels or income brackets. It is important to properly encode categorical class labels before using them in machine learning models, as different encoding methods can affect the performance of the model. One-hot encoding and label encoding are two common methods used to encode categorical class labels.
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