Question
HOTSPOT -
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
• Feature splitting
• Logarithmic transformation
• One-hot encoding
• Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
Explanation
One-hot encoding is appropriate for categorical data like city names, which cannot be ordered or measured numerically. Feature splitting is suitable for composite features such as "Type_year," where separating the type of home and the year it was built can provide clearer insights to the model. Logarithmic transformation is effective for size measurements, as it helps in handling skewed distributions often found with area measurements, making patterns more visible to the model.
