Introduction
Welcome to the session on ‘Multivariate Logistic Regression (Model Evaluation)‘.
In this session
In this session, you will first learn about a few more metrics beyond accuracy that are essential to evaluate the performance of a logistic regression model. Then based on these metrics, you’ll learn how to find out the optimal scenario where the model will perform the best. The metrics that you’ll learn about are
- Accuracy
- Sensitivity, specificity and the ROC curve
- Precision and Recall
Finally, once you’ve chosen the optimal scenario based on the evaluation metrics, you’ll finally go on and make predictions on the test dataset and see how your model performs there as well.
Prerequisites
There are no prerequisites for this session, other than knowledge of the previous two sessions!
Guidelines for in-session questions
The in-video and in-content questions for this module are not graded. Note that graded questions are given on a separate page labelled ‘Graded Questions’ at the end of this session. The graded questions in this session will adhere to the following guidelines:
| First Attempt Marks | Second Attempt Marks | |
| Questions with 2 Attempts | 10 | 5 |
| Questions with 1 Attempt | 10 | 0 |