| Title | Decision trees | |
| Duration | 2 x 45 mins | |
| Module | A | |
| Lesson Type | Lecture | |
| Focus | Practical - AI Modelling | |
| Topic | Data analysis |
Data Set illustration and Preprocessing,Decision Tree,Model Building,Fitting and evaluating a Decision Tree,Cross Validation,
The materials of this learning event are available under CC BY-NC-SA 4.0.
You can base this class around the notebooks by BME on Data Analysis Platforms (HU)
| 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 |
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
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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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