Transforming Categorical Variables

Requirements Clarification & Assessment

Before transforming categorical variables for machine learning models, it is crucial to understand the dataset and the specific needs of the model. Key considerations include:

1. Nature of the Categorical Variables:

   • Nominal Variables: These have categories without any intrinsic order (e.g., color, brand).
   • Ordinal Variables: These have a meaningful order (e.g., rating scales like "poor", "average", "good").

2. Cardinality of Categorical Variables:

   • Low Cardinality: Few unique categories.
   • High Cardinality: Many unique categories, which can complicate encoding.

3. Model Requirements:

   • Some models, like tree-based models, might handle categorical data differently compared to linear models.

4. Data Distribution:

   • Understanding the distribution of categories is essential to choose the right encoding technique.

5. Target Variable:

   • Consider the relationship between categorical variables and the target variable, especially for target encoding.

6. Computational Efficiency:

   • The encoding method should be computationally feasible given the size of the dataset.