Associate Data Practitioner · Free Practice Question Easy
Question 26
A data analyst is using BigQuery ML to predict customer lifetime value. After training the model, which function should they use to assess how well the model is performing?
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A
ML.PREDICT
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B
ML.EVALUATE
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C
ML.TRAINING_INFO
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D
ML.EXPLAIN_PREDICT
Reveal correct answer
Correct answer: B
Explanation
Option B is CORRECT. ML.EVALUATE is specifically designed to assess model performance by calculating evaluation metrics on a test dataset. For a customer lifetime value prediction (regression) model, it would return metrics such as mean squared error (MSE), mean absolute error (MAE), and R-squared. These metrics help the analyst understand how accurately the model is predicting customer lifetime values and whether it's performing well enough for business use.
Option A is INCORRECT. ML.PREDICT generates predictions using the trained model but doesn't evaluate how accurate those predictions are. It applies the model to new data but provides no assessment of performance quality.
Option C is INCORRECT. ML.TRAINING_INFO returns information about the training process, such as iteration details and loss values during training. While useful for understanding training dynamics, it doesn't evaluate the model's predictive performance on new data.
Option D is INCORRECT. ML.EXPLAIN_PREDICT helps understand which features influenced specific predictions but doesn't provide overall performance metrics. It's useful for interpreting individual predictions, not for assessing the model's accuracy as a whole.
Discussion
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