You work for a real estate e-commerce company. Your machine learning team is building a house price prediction model to be used on your company’s site. This model will be used as a guide to users as an unbiased objective estimate of a given house’s value. Your company has gathered an enormous dataset of house observations from across the United States. The observations in the dataset are categorized by region of the country. The housing data prices are mainly clustered by region across the dataset. However, each region has several outlier priced houses.
Since you have defined the housing price prediction work as a regression problem, you have selected the XGBoost SageMaker built-in algorithm to base your model. You are now ready to do your hyperparameter tuning. So you need a good regression evaluation metric. Which of the following evaluation metrics best fit your problem?

Answer options:

A.MSE (Mean Squared Error)
B.AUC (Area Under the Curve)
C.ROC curve (Receiver Operating Characteristic) curve
D.MAE (Mean Absolute Error)

Answer: D
Option A is incorrect. The MSE metric is useful for measuring regression problems. However, it does not handle outliers as well as the MAE metric. Your dataset has several outliers per region.
Option B is incorrect. The AUC metric is best used for classification type machine learning algorithms. You are using a regression algorithm.
Option C is incorrect. The AUC metric is best used for classification type machine learning algorithms. You are using a regression algorithm.
Option D is correct. The MAE is the correct regression metric to use when outliers can significantly influence your dataset. Your dataset contains several outliers per region.
Reference:
Please see the article titled 20 Popular Machine Learning Metrics. Part 1: Classification & Regression Evaluation Metrics, the Amazon SageMaker developer guide titled XGBoost Algorithm, and the Amazon SageMaker developer guide titled Tune an XGBoost Model.