DSA-C03 Exam Dumps | You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model

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Question 4/143

You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

A. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.

B. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.

C. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.

D. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.

E. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.

Correct Answer: B,D,E

The correct answers are B, D, and E. B: One-hot encoding allows you to see the individual effect of each neighborhood. D: ICE plots reveal how the relationship between 'location' and predicted price varies for different individual instances, highlighting potential heterogeneity. E: A two-way PDP with 'location' and 'square_footage' helps understand if the effect of location is different for houses of different sizes. Removing 'location' (option A) might decrease performance if it's a relevant feature. Replacing it with average price (option C) introduces potential bias and data leakage if the historical data is used for both training and validation.

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