You are a machine learning specialist on a software development team in a real estate company. Your management team has asked your team to build a logistic regression model that your company wishes to use to predict whether or not a person will buy a given listing based on multiple attributes of the sale, the property, and the customer profile. Your team lead has assigned your team to find the optimal model with an ideal classification threshold.
Which model evaluation technique should your team use to discover how different classification thresholds will affect the model`s performance?

Answer options:

A.Rand Index
B.Root Mean Square Error (RMSE)
C.Mean Absolute Error (MAE)
D.Receiver Operating Characteristic (ROC) curve

Answer: D
Option A is incorrect. The Rand Index evaluation technique is used for optimizing unsupervised models, not supervised models like logistic regression models.
Option B is incorrect. The RMSE evaluation technique is used for regression problems where you are solving for a continuous variable. In this use case, you are solving a binary classification: will or will not buy.
Option C is incorrect. The MAE evaluation technique is also used for regression problems where you are solving for a continuous variable. In this use case, you are solving a binary classification: will or will not buy.
Option D is CORRECT. The Receiver Operating Characteristic curve evaluation technique is used for regression problems where you are solving for a binary variable. In this use case, you are solving a binary classification: will or will not buy.
Reference:
Please see the Google Machine Learning Crash Course article titled Classification: ROC Curve and AUC.Please refer to the Towards Data Science article titled Understanding AUC - ROC Curve.
Please review the Data Institute article titled Choosing the Right Metric for Evaluating Machine Learning Models — Part 1.