MLS-C01 Question 104
Select 3You are building a machine learning model to predict customer churn for an e-commerce platform. The dataset contains a categorical feature called 'Subscription_Type' with values such as 'Free', 'Basic', 'Premium', and 'Enterprise'. Additionally, there is a continuous feature called 'Account_Age' (in days). During exploratory data analysis, you notice extreme outliers in the 'Account_Age' feature. Which of the following steps should you take to appropriately prepare these features for the model?
- A
Apply one-hot encoding to the 'Subscription_Type' feature.
- B
Perform binning on the 'Account_Age' feature to group values into intervals.
- C
Ignore the outliers in the 'Account_Age' feature as machine learning algorithms can handle them automatically.
- D
Replace the outliers in 'Account_Age' with the median value of the feature.
- E
Convert the 'Subscription_Type' feature into numerical labels using label encoding.
Show answer and explanation
Correct answers: A, B, D
Explanation
Feature engineering is a critical step in preparing data for machine learning models. For the categorical feature 'Subscription_Type', one-hot encoding is the most appropriate method to avoid introducing false ordinal relationships. For the continuous feature 'Account_Age', extreme outliers should be addressed to prevent them from negatively impacting the model's performance. Techniques like replacing outliers with the median value or binning the feature into intervals are effective strategies to handle such scenarios. Ignoring outliers or using inappropriate encoding methods can lead to suboptimal model performance.
- A. Correct.
Correct. One-hot encoding is an appropriate method for handling a categorical feature like 'Subscription_Type' to ensure it is represented numerically without introducing ordinal relationships.
- B. Correct.
Correct. Binning can help reduce the impact of extreme outliers by grouping continuous values into discrete intervals, which might be beneficial for certain algorithms or business interpretations.
- C. Incorrect.
Incorrect. Ignoring outliers is not recommended as they can negatively impact the performance of machine learning models, especially algorithms sensitive to extreme values.
- D. Correct.
Correct. Replacing outliers with the median value is a common technique to mitigate the impact of extreme values without losing too much information.
- E. Incorrect.
Incorrect. Label encoding is not suitable in this case as it introduces ordinal relationships between categorical values (e.g., 'Free' < 'Basic' < 'Premium'), which do not exist naturally in 'Subscription_Type'.