DSA-C03 Exam Dumps | You are developing a model to predict customer churn using Snowflake ML. After training a Gradient Boosting

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Question 135/143

You are developing a model to predict customer churn using Snowflake ML. After training a Gradient Boosting model, you want to understand the relationship between 'number_of_products' and the churn probability. You generate a partial dependence plot (PDP) for 'number_of_products'. The PDP shows a steep increase in churn probability as 'number_of_products' increases from 1 to 3, followed by a plateau. Which of the following statements are the MOST accurate interpretations of this PDP? Assume the dataset is balanced and has undergone proper preprocessing.

A. Customers who purchase more than 3 products are less likely to churn, suggesting higher engagement or satisfaction.

B. The model is perfectly calibrated, and the PDP accurately represents the true causal effect of 'number_of_products' on churn.

C. There might be a confounding variable correlated with both 'number_of_products' and churn, leading to a spurious relationship in the PDP.

D. The PDP indicates a high degree of interaction between 'number_of_products' and other features in the model, making the interpretation unreliable.

E. Increasing the number of products purchased by all customers will definitively reduce overall churn.

Correct Answer: A,C

The correct answers are A and C. A: The plateau after 3 products indicates that increasing purchases beyond this point doesn't significantly reduce churn. C: PDPs show correlation, not causation. A confounding variable could be driving both 'number_of_products' and churn. Option B is incorrect because no model is perfectly calibrated and PDPs don't represent causal effects without further analysis. Option D is plausible but requires more information about the specific model and feature interactions. Option E is incorrect as PDPs indicate correlation and not necessarily causation, thus, it would be unsafe to assume increasing the number of products would definitively reduce churn.

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