Your work for a company that performs seismic research for client firms that drill for petroleum. As a machine learning specialist, you have built a series of models that classify seismic waves to determine the seismic profile of a proposed drilling site. You need to select the best model to use in production. Which metric should you use to compare and evaluate your machine learning classification models against each other?

Answer options:

A.Area Under the ROC Curve (AUC)
B.Mean square error (MSE)
C.Mean Absolute Error (MAE)
D.Recall

Correct Answer: A
Option A is correct. The area under the Receiver Operating Characteristic (ROC) curve is the most commonly used metric to compare classification models.
Option B is incorrect. The Mean Square Error (MSE) is commonly used to measure regression error. It finds the average squared error between the predicted and actual values. It is not used to compare classification models.
Option C is incorrect. The Mean Square Error is also commonly used to measure regression error. It finds the average absolute distance between the predicted and target values. It is not used to compare classification models.
Option D is incorrect. The recall metric is the percentage of results correctly classified by a model. This metric alone will not allow you to make a complete assessment and comparison of your models.
References:
Please see the Towards Data Science article titled Metrics For Evaluating Machine Learning Classification Models (https://towardsdatascience.com/metrics-for-evaluating-machine-learning-classification-models-python-example-59b905e079a5),
The Towards Data Science article titled How to Evaluate a Classification Machine Learning Model (https://towardsdatascience.com/how-to-evaluate-a-classification-machine-learning-model-d81901d491b1),
The Machine Learning Mastery article titled Assessing and Comparing Classifier Performance with ROC Curves (https://machinelearningmastery.com/assessing-comparing-classifier-performance-roc-curves-2/), 
The Towards Data Science article titled 20 Popular Machine Learning Metrics. Part 1: Classification & Regression Evaluation Metrics (https://towardsdatascience.com/20-popular-machine-learning-metrics-part-1-classification-regression-evaluation-metrics-1ca3e282a2ce),
The Data School article titled Simple guide to confusion matrix terminology (https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/),
The Medium article titled Precision vs. Recall (https://medium.com/@shrutisaxena0617/precision-vs-recall-386cf9f89488)