MIS Data Mining Practice Test

Label encoding is a technique used to convert categorical variables into a numerical format that can be more easily processed by machine learning algorithms. The primary function of label encoding is to replace each category with a unique integer. This allows the model to interpret categorical data as numeric input.

For instance, if you have a categorical variable representing colors, such as "red," "blue," and "green," label encoding would assign unique integers to each color—such as 0 for "red," 1 for "blue," and 2 for "green." This transformation retains the distinct identity of each category while enabling computational efficiency during model training.

While other methods, such as one-hot encoding, are used to convert categories into a binary matrix representation, they serve a different purpose. Label encoding is particularly useful when the categorical variable has a natural order, such as ratings or scores. By interpreting the categories as unique integers, label encoding helps maintain an efficient representation of categorical data for various machine learning techniques.