Administrative information

Title	Decision trees
Duration	2 x 45 mins
Module	A
Lesson Type	Practical
Focus	Practical - AI Modelling
Topic	Data analysis

Keywords

Dataset generation,Fitting,Model complexity,

Learning Goals

learn basics of decision trees

Expected Preparation

Learning Events to be Completed Before

Lecture: Decision Trees

Obligatory for Students

Lecture: Data Preparation and Exploration
Lab session: Data Preparation and Exploration

Optional for Students

None.

References and background for students:

Scikit-Learn
Keras
Decision Tree on wikipedia
Ron Kohavi, "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996.
https://scikit-learn.org/stable/modules/tree.html
https://pulplearning.altervista.org/algoritmi-di-machine-learning-decision-tree/
https://www.kaggle.com/code/hamelg/python-for-data-29-decision-trees/notebook
https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052

Recommended for Teachers

None.

Lesson Materials

Notebook for Decision Tree

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

This learning event consist of laboratory tasks that shall be solved by the students with the help of the leading instructor.

You can base this class around the notebooks.

Outline/time schedule


Duration (min)	Description	Concepts	Activity	Material
5	Brief of the tasks to be conducted	Introduction	Lecture
5	Generating a two-dimensional dataset	Gaussian noise, np.random.randn()	Coding	Jupyter notebook
10	Fitting and evaluating a Decision Tree	scikit-learn	Coding
15	Investigating the effect of the model complexity parameter	model complexity, support vectors, margin, plotting	Documentation
5	Fitting the Model	Model Fitting	Coding
15	Evaluate and Investigatie the effect of the parameter	Parameters Evaluation	Documentation
20	Implementing a custom precomputed Model	matrix operations in numpy	Coding
45	Fitting and evaluating the model on real data	data preprocessing (scaling), Accuracy, Confusion Matrix, Cross Validation	Documentation

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

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities