Intermediate
Artificial Intelligence & Machine Learning
Q69 / 100
Why is one-hot encoding commonly used for categorical features in machine learning?
Correct! Well done.
Incorrect.
The correct answer is B) It converts categories into binary indicator columns, avoiding a false sense of ordinal relationship that plain integer encoding would imply to algorithms that assume numeric ordering
B
Correct Answer
It converts categories into binary indicator columns, avoiding a false sense of ordinal relationship that plain integer encoding would imply to algorithms that assume numeric ordering
Explanation
Assigning integers like 1, 2, 3 to unordered categories implies an ordering or magnitude that does not exist. One-hot encoding creates a separate binary column per category, letting the model treat them as independent without artificial ordinal relationships.
Progress
69/100