Administrative Information
| Title | Decision trees |
| Duration | 2 x 45 mins |
| Module | A |
| Lesson Type | Lecture |
| Focus | Practical - AI Modelling |
| Topic | Data analysis |
Keywords
Data Set illustration and Preprocessing,Decision Tree,Model Building,Fitting and evaluating a Decision Tree,Cross Validation,
Learning Goals
- learn basics of decision trees
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- N/A
Optional for Students
- N/A
References and background for students
Recommended for Teachers
- N/A
Lesson materials
Instructions for Teachers
You can base this class around the notebooks by BME on Data Analysis Platforms (HU)
Outline/time schedule
| Duration (min) | Description | Concepts | Activity | Material |
|---|---|---|---|---|
| 5 | Brief of the tasks to be conducted | Lecture | ||
| 10 | Data Set illustration and Preprocessing | Data Preprocessing | Coding | Jupyter notebook |
| 10 | Definition of a Decision Tree | scikit-learn: Decision Tree | Coding | |
| 20 | Model Building | model complexity, plotting | Documentation | |
| 15 | Fitting and evaluating a Decision Tree | Fit | Coding | |
| 10 | Cross Validation | Cross Validation | Documentation | |
| 15 | Model Evaluation | operations in numpy, data preprocessing (scaling), Accuracy | Coding | |
| 5 | Concluding Remarks | Documentation |
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.