Administrative information
| Title |
Model Evaluation |
 |
| Duration |
60 |
| Module |
A |
| Lesson Type |
Lecture |
| Focus |
Technical - Foundations of AI |
| Topic |
Foundations of AI |
Keywords
underfitting, overfitting, generalization, bias-variance decomposition, model complexity, ROC curve,
Learning Goals
- Learners understand the concept of expected predictive performance and generalization
- Learners understand the concept of model complexity and its relationship with generalization performance
- Learners are familiar with bias and variance their relationship with underfitting and overfitting
- Learners have a firm grasp on evaluating binary classification models with the most widely applied metrics
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
None.
Optional for Students
References and background for students:
None.
Recommended for Teachers
- Familiarize themselves with the demonstration materials.
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
Instructions for Teachers
Cover the topics in the lesson outline and demonstrate the concepts using the interactive notebooks (effect of the hyperparameters on under/overfitting and the bias/variance curves; calcluation of the ROC/PR curves).
Outline/time schedule
| Duration (min) |
Description |
Concepts |
| 5 |
Non-linear regression (recap) |
basis points, RBF, kernel parameters, mean squared error |
| 10 |
Model complexity, regulariztaion and the number of parameters |
complexity, regularization |
| 10 |
Bias-variance decomposition of mean squared error |
generalization error, bias, variance, observation noise, underfitting, overfitting |
| 10 |
Demonstration of the effects of the complexity parameter,
regularization coefficient and the number of basis points on
curve fitting and bias/variance
|
|
| 15 |
Evaluation of classification models |
confusion matrix, TPR, FPR, precision, decision boundary, ROC/PR curve |
| 10 |
Demonstration of decision boundaries and the ROC curve |
|
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
 |
The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.
The materials of this learning event are available under CC BY-NC-SA 4.0
|
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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