Why is categorical encoding necessary?
The primary reason to perform categorical encoding is that ML algorithms require numerical input. Most ML algorithms are mathematical models that perform operations on numerical data. They calculate distances, optimize weights, and apply mathematical transformations that require numerical input. As a result, categorical data (text values) must be converted into numbers before being fed into these models. For example, in a logistic regression model, the algorithm needs to calculate the likelihood of an outcome based on input features. If these features are non-numeric, the model cannot perform the necessary calculations.
Another reason is for model interpretation and decision-making. For some models, such as decision trees, the ability to split data based on numeric thresholds is essential. Categorical data must be encoded numerically to allow the model to create meaningful splits and make decisions at each node of the tree. Encoding helps preserve...