Overview

In this module, we will look at evaluation.

Module Objectives

At the end of this unit, you will be able to

Evaluate the technical performance of regression-based prediction models using tools such as Mean Absolute Error, RMSE, and Adjusted R^2. (CLO B, C)
Evaluate the technical performance of classifiers using a variety of tools such as confusion matrices, ROC curves, AUROC, accuracy, kappa statistics, PPV, sensitivity, and specificity balanced accuracy, f1-scores, Matthew's Correlation Coefficient, gain tables and lift charts, etc. (CLO B, C )
Identify when accuracy is an inappropriate metric for classification models. (CLO A, C)

Each Week (Module) Objective in this course has its associated course-level objective (CLO) indicated to clarify the relationship between the two.

Read the textbook: Fundamentals of Machine Learning for Predictive Analytics
- Chapter 9: Evaluation (Section 9.4.2 to end of chapter -- pages 547 - 587)