You work as a machine learning specialist for a scientific instruments company.
Your machine learning team has been assigned the task of developing a machine learning model that optimizes the electronic components in the production line to the current product development lifecycle.
You have built your model using the XGBoost SageMaker built-in algorithm.
You are now in the process of tuning the model hyperparameters.
In your hyperparameter job, you have chosen the num_class, num_round, alpha, booster, early_stopping_rounds, min_child_weight, subsample, eta, and num_round hyperparameters to use in your optimization.
When running your hyperparameter tuning job, you have noticed that the computational complexity of a hyperparameter tuning job is high.
You are getting suboptimal results.
Which option should you implement to get better results from your hyperparameter tuning job?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect.
The alpha hyperparameter is one of the hyperparameters that have the greatest effect on optimizing the XGBoost evaluation metrics.
Therefore, removing it will not help optimize your hyperparameter tuning job.
Option B is incorrect.
The eta hyperparameter is one of the hyperparameters that have the greatest effect on optimizing the XGBoost evaluation metrics.
Therefore, removing it will not help optimize your hyperparameter tuning job.
Option C is correct.
Adjusting (limiting) the range of values that you search for each of the hyperparameters to a smaller range of values can significantly improve the success of hyperparameter optimization.
Option D is incorrect.
Adjusting (expanding) the range of values that you search for each of the hyperparameters to a larger range of values will most likely make your hyperparameter optimization less optimal.
References:
Please see the Amazon SageMaker developer guide titled How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html),
The Amazon SageMaker developer guide titled Best Practices for Hyperparameter Tuning (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-considerations.html),
The Amazon SageMaker developer guide titled XGBoost Hyperparameters (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html),
The Amazon SageMaker developer guide titled Tune an XGBoost Model (https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost-tuning.html),
The Amazon SageMaker developer guide titled Example: Hyperparameter Tuning Job (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex.html)
Hyperparameter tuning is an essential step in machine learning model development as it helps to improve the performance of a model by finding the optimal set of hyperparameters. However, tuning hyperparameters can be a computationally expensive process, particularly when dealing with complex models like XGBoost.
In this scenario, the machine learning specialist has built a machine learning model using the XGBoost SageMaker built-in algorithm and is currently in the process of tuning the hyperparameters. The hyperparameters that have been selected for the hyperparameter tuning job are num_class, num_round, alpha, booster, early_stopping_rounds, min_child_weight, subsample, eta, and num_round.
However, the machine learning specialist has noticed that the computational complexity of the hyperparameter tuning job is high, and the results are suboptimal. Therefore, the specialist needs to take steps to improve the results of the hyperparameter tuning job.
Option A suggests reducing the number of hyperparameters in the optimization by removing the alpha hyperparameter from the hyperparameter tuning job. However, this may not be the best approach since alpha is a regularization hyperparameter that controls the L1 regularization of the model. Removing this hyperparameter may result in a model that overfits the training data and performs poorly on unseen data. Therefore, option A is not the correct answer.
Option B suggests reducing the number of hyperparameters in the optimization by removing the eta hyperparameter from the hyperparameter tuning job. Eta is a learning rate hyperparameter that controls the step size of the gradient descent algorithm used in XGBoost. Reducing the eta hyperparameter may lead to faster convergence of the gradient descent algorithm, but it may also result in a suboptimal model. Therefore, option B may not be the best approach to improve the results of the hyperparameter tuning job.
Option C suggests adjusting the range of values for each of the hyperparameters to a smaller range of values. This option can be a useful approach to improve the results of the hyperparameter tuning job as it helps to reduce the computational complexity of the job by limiting the search space. A smaller range of values may also help the hyperparameter tuning algorithm to focus on the most promising hyperparameters. Therefore, option C is a reasonable approach to improve the results of the hyperparameter tuning job.
Option D suggests adjusting the range of values for each of the hyperparameters to a larger range of values. However, this approach is not recommended as it will increase the computational complexity of the hyperparameter tuning job, making it even more challenging to find the optimal set of hyperparameters.
In summary, to improve the results of the hyperparameter tuning job for the XGBoost SageMaker built-in algorithm, the machine learning specialist should consider adjusting the range of values for each of the hyperparameters to a smaller range of values (option C). This approach will help to reduce the computational complexity of the job and focus the hyperparameter tuning algorithm on the most promising hyperparameters.