MLA-C01 Question 61
Single answerA data scientist is working on a machine learning model to predict customer churn for a subscription-based service. The dataset contains a categorical feature called 'Subscription Plan' with three possible values: 'Basic', 'Standard', and 'Premium'. The data scientist needs to encode this feature to use it in a gradient-boosted tree model. Which encoding technique would be the most appropriate to ensure the model performs optimally?
- A
One-hot encoding
- B
Label encoding
- C
Binary encoding
- D
Tokenization
Show answer and explanation
Correct answer: A
Explanation
Gradient-boosted tree models can handle sparse features produced by one-hot encoding effectively. Since the 'Subscription Plan' feature is categorical with no inherent order, one-hot encoding is the best choice to ensure that the model does not infer any ordinal relationships and treats each category as independent. Other encoding methods like label encoding or binary encoding may introduce unintended ordinal relationships, which could negatively impact the model's performance.
- A. Correct.
One-hot encoding is the most appropriate for categorical features when using models like gradient-boosted trees. It avoids introducing ordinal relationships between categories and ensures the model treats each category independently. Gradient-boosted tree models can handle the sparse representation produced by one-hot encoding.
- B. Incorrect.
Label encoding assigns numeric labels to each category (e.g., 0, 1, 2). However, this introduces an ordinal relationship between categories, which can mislead the model into assuming a ranking or hierarchy that doesn't exist.
- C. Incorrect.
Binary encoding is a more memory-efficient encoding method that combines aspects of one-hot and label encoding. While it can be useful in some cases, it may still introduce unintended ordinal relationships between categories, which are not ideal for this use case.
- D. Incorrect.
Tokenization is a technique used to preprocess text data by splitting it into smaller units like words or subwords. It is not applicable for encoding categorical variables in this scenario.