Google Professional Machine Learning Engineer Question 226
Select 3Google Cloud PlatformYou are training a machine learning model on Google Cloud to predict customer churn for a subscription-based service. The dataset has 10 million records with a mix of numerical, categorical, and text features. You notice that the training time is significantly longer than expected, and the model's accuracy is not improving after a certain number of epochs. What steps should you take to address these issues?
- A
Perform feature engineering to reduce the dimensionality of the dataset.
- B
Use a smaller training dataset to speed up model training.
- C
Enable early stopping to prevent overfitting and save training time.
- D
Optimize your model architecture or hyperparameters, such as the learning rate.
- E
Switch to a more complex model to better capture the dataset's patterns.
Show answer and explanation
Correct answers: A, C, D
Explanation
Training machine learning models efficiently requires careful consideration of the dataset, model architecture, and training process. Reducing dimensionality through feature engineering can simplify the data, speeding up training and improving performance. Early stopping prevents wasted computational resources by halting training when no further improvements are observed. Hyperparameter tuning ensures the model trains effectively and converges to an optimal solution. However, using a smaller dataset or a more complex model are not ideal solutions in this scenario as they may lead to underfitting or increased training time without guaranteeing better performance.
- A. Correct.
Feature engineering can help reduce the complexity of the dataset by eliminating irrelevant or redundant features, which can significantly improve training speed and model performance.
- B. Incorrect.
Using a smaller training dataset can speed up training, but it may lead to underfitting or loss of critical patterns in the data. This is not a recommended solution unless computational resources are severely limited.
- C. Correct.
Early stopping is a common technique to prevent overfitting and save training time by halting the training process once the validation performance stops improving.
- D. Correct.
Optimizing the model architecture or hyperparameters, such as learning rate, can improve training efficiency and model performance by ensuring the model converges effectively.
- E. Incorrect.
Switching to a more complex model may exacerbate the training time issue and increase the risk of overfitting, especially if the current model's complexity is already sufficient for the task.