You work in the machine learning department of a major retail company. Your team is working on a model to classify customers by purchase history. Your marketing department wants to use the results of your model predictions to determine which customers should receive a new campaign offer. You have selected your observations and cleaned your data. You have also split your data into training and evaluation datasets. You are now training your k-means model in Amazon SageMaker, and you are trying to select the model hyperparameters that give your marketing team the best predictions.
You have set the feature_dim hyperparameter to equal the number of features in your input data. You have set the k hyperparameter to 10. The number of clusters you estimate is appropriate for your model. You have set the epochs hyperparameter to 1 so that the model performs one pass over your data.
You need to report a score for your model. Which k-means hyperparameter allows you to select the metric types to report this scoring, and what are the available metric options?

Answer options:

A.extra_center_factor with msd, ssd, or [msd, ssd] as the available metric type values
B.score_metrics with mse, ssd, or [mse, ssd] as the available metric type values
C.eval_method with mse, ssd, or [mse, ssd] as the available metric type values
D.eval_metrics with msd, ssd, or [msd, ssd] as the available metric type values

Answer: D
Option A is incorrect. The hyperparameter you chose to report a score for your model is the eval_metrics hyperparameter. The eval_metrics hyperparameter has the allowed values of msd for Mean Square Error, ssd for Sum of Square Distance, and the option of both msd and ssd. The extra_center_factor is used to control the number of clusters.
Option B is incorrect. The hyperparameter you chose to report a score for your model is the eval_metrics hyperparameter. The eval_metrics hyperparameter has the allowed values of msd for Mean Square Error, ssd for Sum of Square Distance, and the option of both msd and ssd. The Amazon SageMaker k-means algorithm does not have a score_metrics hyperparameter.
Option C is incorrect. The hyperparameter you chose to report a score for your model is the eval_metrics hyperparameter. The eval_metrics hyperparameter has the allowed values of msd for Mean Square Error, ssd for Sum of Square Distance, and the option of both msd and ssd. The Amazon SageMaker k-means algorithm does not have an eval_method hyperparameter.
Option D is correct. The hyperparameter you chose to report a score for your model is the eval_metrics hyperparameter. The eval_metrics hyperparameter has the allowed values of msd for Mean Square Error, ssd for Sum of Square Distance, and the option of both msd and ssd.
Reference:
Please see the Amazon SageMaker developer guide titled K-Means Hyperparameters.