**Python Machine Learning**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.**Evaluating a Machine Learning Model**Jordan, J.(2017). Evaluating a Machine Learning Model.

**This article describes how to determine a machine learning model is performing optimally.****Metrics to Evaluate a Machine Learning Algorithm**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).****Choosing the Right Metric for Evaluating Machine Learning Models – Part 1**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).****Choosing the Right Metric for Evaluating Machine Learning Models – Part 2**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.**

- Last Updated: Oct 12, 2022 9:17 AM
- URL: https://resources.nu.edu/c.php?g=1105980
- Print Page