Optimizing Container Truck Routes
Question
You work for a transportation company as a machine learning specialist.
You are currently working on a project to optimize container truck routes to minimize empty container travel.
For example, as a truck delivers its payload to a destination, you want to have the container loaded for another route.
You don't want the truck to move to another destination with an empty container.
You have selected the SageMaker XGBoost algorithm for your model.
You now need to tune your hyperparameters to get the optimum performance out of your model.
You have chosen the Area Under the Curve (AUC) metric as your objective metric for your hyperparameter tuning job. Which algorithm should you use as the SageMaker hyperparameter tuning algorithm to get your results in the minimal number of training jobs?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect.
SageMaker uses two types of models to search for the optimum hyperparameters for your model: Random Search and Bayesian Search.
For most models, Bayesian Search requires less training jobs to reach your optimal hyperparameter settings.
(See the Amazon Machine Learning blog titled Amazon SageMaker automatic model tuning now supports random search and hyperparameter scaling)
Option B is correct.
SageMaker uses two types of models to search for the optimum hyperparameters for your model: Random Search and Bayesian Search.
For most models, Bayesian Search requires less training jobs to reach your optimal hyperparameter settings.
(See the Amazon Machine Learning blog titled Amazon SageMaker automatic model tuning now supports random search and hyperparameter scaling)
Option C is incorrect.SageMaker hyperparameter tuning does not use Linear Search as a hyperparameter tuning model.
Option D is incorrect.
SageMaker hyperparameter tuning does not use Depth First Search as a hyperparameter tuning model.
Reference:
Please see the Amazon SageMaker developer guide titled Configure and Launch a Hyperparameter Tuning Job, the Amazon SageMaker developer guide titled Automatic Model Tuning, and the Amazon SageMaker developer guide titled How Hyperparameter Tuning Works.
The correct answer for this question is B. Bayesian Search.
SageMaker is a managed service offered by AWS for building, training, and deploying machine learning models at scale. It offers various algorithms for hyperparameter tuning, including Random search, Bayesian Search, Linear Search, and Depth First Search.
Hyperparameter tuning is a critical process in machine learning as it helps to find the optimal values for the model's hyperparameters, which are not learned from the data but set by the developer beforehand. These hyperparameters play a significant role in the model's performance, and tuning them is essential to achieve the best possible accuracy or other performance metrics.
In this scenario, the objective is to optimize container truck routes to minimize empty container travel, and the model's performance metric is the Area Under the Curve (AUC). AUC is a widely used performance metric for binary classification problems, and it measures the ability of the model to distinguish between positive and negative examples.
Out of the given options, Bayesian Search is the best algorithm to use as the SageMaker hyperparameter tuning algorithm to get the results in the minimal number of training jobs. The main reason is that Bayesian Search uses probabilistic models to approximate the objective function and choose the next set of hyperparameters to evaluate. This approach can lead to faster convergence and better results with fewer training jobs than other search algorithms, such as Random search or Linear search.
Random search is a simple search algorithm that samples hyperparameters randomly from the search space, and it can be useful for finding global optima. However, it requires a large number of training jobs to converge, which may not be practical in this scenario.
Linear search is another simple algorithm that performs a systematic search over the hyperparameter space by trying values at fixed intervals. It is less efficient than Bayesian Search since it requires a higher number of training jobs to converge.
Depth First Search is not a hyperparameter tuning algorithm, but rather a graph traversal algorithm, and it is not applicable to this scenario.
In summary, Bayesian Search is the best algorithm to use as the SageMaker hyperparameter tuning algorithm to get the results in the minimal number of training jobs, given the scenario and the objective metric.
