PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #317
You work for a large retailer, and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics purchase history, and…
The correct answer is D. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to. Logistic regression is the appropriate choice for predicting customer churn, which is a binary classification problem. BigQuery ML supports evaluation of classification models using ML.CONFUSION_MATRIX, which provides key metrics like precision, recall, and accuracy - essential…
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Options
- ACreate a linear regression model in BigQuery ML, and register the model in Vertex AI Model
- BCreate a logistic regression model in BigQuery ML, and register the model in Vertex AI Model
- CCreate a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the
- DCreate a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to
How the community answered
(57 responses)- A9% (5)
- B2% (1)
- C5% (3)
- D84% (48)
Explanation
Logistic regression is the appropriate choice for predicting customer churn, which is a binary classification problem. BigQuery ML supports evaluation of classification models using ML.CONFUSION_MATRIX, which provides key metrics like precision, recall, and accuracy - essential for thorough performance evaluation in this context.
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