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Answer: Use BQML XGBoost regression to train the model.
The correct answer is B: 'Use BQML XGBoost regression to train the model.' This approach leverages BigQuery Machine Learning (BQML) and the XGBoost algorithm, which is optimized for regression tasks. BQML requires minimal effort for setting up and training models due to its integration with BigQuery, thus reducing training time significantly. Option A would require more effort in setting up a custom TensorFlow model. Option C is less preferred because it could take longer without early stopping. Option D is not suitable because RMSLE as an optimization objective does not support negative values in the target variable.
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Given a dataset containing 50,000 records stored in BigQuery, where the data includes 20 categorical and numerical features and a target variable that can include negative values, you need to train a regression model. The primary goals are to minimize effort and training time while maximizing model performance. Which of the following approaches should you take to train this regression model?
A
Create a custom TensorFlow DNN model
B
Use BQML XGBoost regression to train the model.
C
Use AutoML Tables to train the model without early stopping.
D
Use AutoML Tables to train the model with RMSLE as the optimization objective.
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