TIM-6500: Principles of Data Science

Lee, W. (2019). Python machine learning. ProQuest Ebook Central.
Read Chapter 12, Deploying Machine Learning Models, Evaluating the Algorithms (pp. 277-280).
This chapter provides a comprehensive case study and evaluates different algorithms to determine the best performing one.

Jordan, J.(2017). Evaluating a Machine Learning Model.
This article describes how to determine a machine learning model is performing optimally.

Mishra, A. (2018). Metrics to evaluate a machine learning algorithm.
This article describes different metrics, such as classification accuracy, logarithmic loss, confusion matrix, area under the curve (AUC), and mean squared error (MSE).

Swalin, A.(2018). Choosing the right metric for evaluating machine learning models - Part 1.
This article (part 1 of 2) describes useful regression metrics, explicitly emphasizing the root mean square error (RSME) and mean average error (MAE).

Swalin, A.(2018). Choosing the right metric for evaluating machine learning models - Part 2.
This article (part 2 of 2) describes how to determine a machine learning model is supporting a classification problem.