Google Professional Machine Learning Engineer Question 58
Select 3Google Cloud PlatformYou are a Machine Learning Engineer tasked with using AutoML Tables to train a model for predicting customer churn in your organization. During the training process, you notice that the model's accuracy is lower than expected. Which of the following actions should you take to improve the model's performance?
- A
Ensure that the dataset has a balanced representation of churned and non-churned customers.
- B
Manually tune the hyperparameters of the AutoML model to optimize performance.
- C
Analyze the dataset to identify and address missing or inconsistent data.
- D
Increase the size of the training dataset by adding more relevant examples.
- E
Export the trained model and fine-tune it using a custom TensorFlow model.
Show answer and explanation
Correct answers: A, C, D
Explanation
To improve the performance of a model trained using AutoML, you should focus on ensuring data quality, addressing class imbalances, and providing more relevant training data. These actions directly impact the model's ability to learn effectively. Manual hyperparameter tuning and external fine-tuning are unnecessary or counterproductive when using AutoML, as it is designed to handle these optimizations automatically.
- A. Correct.
Ensuring a balanced dataset improves the model's ability to generalize for both churned and non-churned customers, reducing bias in training.
- B. Incorrect.
AutoML automatically handles hyperparameter tuning, and manual intervention is unnecessary and not supported in this context.
- C. Correct.
Addressing missing or inconsistent data improves data quality, directly impacting the model’s ability to learn patterns effectively.
- D. Correct.
Adding more relevant examples to the training dataset increases the amount of information available for the model to learn, improving performance.
- E. Incorrect.
Exporting and fine-tuning the model outside AutoML contradicts the goal of relying on AutoML for training and optimization.