Ordinal Encoding

Pros

• Simple to understand and implement.
• Does not increase the feature space.

Cons

• Imposes an arbitrary order on categories, which may not represent their true relationship.
• Not suitable for non-ordinal categories (i.e., categories that do not have a natural order).

Use Cases

• Suitable for ordinal features, i.e., categories that have a natural order (e.g., ratings, size).
• Useful when simplicity and computational efficiency are more important than capturing complex relationships between categories.

Similarity Encoding

Pros

• Captures more complex relationships between categories by considering their similarity.
• Can handle unseen categories during training.

Cons

• More complex to understand and implement.
• Can significantly increase the feature space, leading to higher computational cost.

Use Cases

• Suitable for non-ordinal features, i.e., categories that do not have a natural order (e.g., city names, product types).
• Useful when it’s important to capture complex relationships between categories, even at the cost of increased computational complexity.