You work as a machine learning specialist for an alternative transportation ride-share company. Your company has scooters, electric longboards, and other electric personal transportation devices in several major cities across the US. Your machine learning team has been asked to produce a machine learning model that classifies device preference by trip duration for each of the available personal transportation devices you offer in each city. You have created a model based on the SageMaker built-in K-Means algorithm. You are now using hyperparameter tuning to get the best-performing model for your problem. Which evaluation metrics and corresponding optimization direction should you choose for your automatic model tuning (a.k.a. hyperparameter tuning)? (Select TWO)

Answer options:

A.msd, maximize
B.mse, minimize
C.ssd , minimize
D.f1, maximize
E.msd, minimize

Correct Answers: C and E
Option A is incorrect. K-Means uses the msd (Mean Squared Distances) metric for model validation. However, you will want to minimize this metric.
Option B is incorrect. K-Means does not use the mse (Mean Squared Error) metric for model validation.
Option C is correct. K-Means uses the ssd (Sum of the Squared Distances) metric for model validation, and you will want to minimize this metric.
Option D is incorrect. K-Means does not use the f1 (weighted average of precision and recall) metric for model validation.
Option E is correct. K-Means uses the msd (Mean Squared Distances) metric for model validation, and you will want to minimize this metric.
References:
Please see the Amazon SageMaker developer guide titled Define Metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-define-metrics.html),
The Amazon SageMaker developer guide titled Tune a K-Means Model (https://docs.aws.amazon.com/sagemaker/latest/dg/k-means-tuning.html)