Topic 1 Question 236
You work for a large retailer, and you need to build a model to predict customer chum. The company has a dataset of historical customer data, including customer demographics purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?
Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
Create a logistic regression model in BigQuery ML and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.
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コメント(8)
- 正解だと思う選択肢: B
B. Linear regression because customer churn is a number of customers (not just 1/0). The key here imo is "thoroughly evaluate performance", which Vertex AI seems to be better suited for than BQ (including the possibility of tracking experiment lineage, inspecting parameter selection of each run, etc)
👍 4b1a8fae2024/01/17 - 正解だと思う選択肢: B
logistic since it's classification, and Vertex AI because we need to "thoroughly evaluate its performance"
👍 4gscharly2024/04/20 - 正解だと思う選択肢: B
B is the definitive answer. By breaking down the question we know it is a classification problem, so A and C are wrong since they're linear regression.
Using confusion matrix to evaluate the model is not wrong (actually it's even the textbook answer to do it), but it is not enough if you want to thoroughly evaluate its performance. Hence the best way to do it is with Vertex AI.
👍 3Dirtie_Sinkie2024/09/17
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