Objectives

Students will be able to:

  • understand a decision tree is a model that makes predictions by repeatedly splitting data into branches based on feature tests, ending in leaf nodes that represent the final decision or class.

Supervised Learning

Supervised learning is a type of machine learning where a model is trained on labelled data — examples that already include the correct answers — so it can learn to predict the right output for new, unseen inputs.

Machine Learning

Theme A4

Decision Trees

A decision tree is a machine learning model used to make predictions or classifications by splitting a dataset into smaller groups based on the values of input features.

Worksheet

Make a copy of the worksheet and move it into your Theme A4 folder.

Watch the video and complete the Worksheet

Buffer Your Notes

Now that you have some knowledge of the nature of Decision Trees, is a useful resource you can use to consolidate your knowledge.

Take some time to explore this resource. There may be opportunities to develop your understanding and buffer your notes on Decision Trees!

Advantages and Limitations

is a useful resource.

Find the

  • Learning Center

Look at the Advantages and Limitations section. Make notes in your Workbook or paste a screenshot of this useful revision info!

Real World Applications

Using the previous resource:

Find the

  • Learning Center

Find the Real World Applications section. Make notes in your Workbook or paste a screenshot of this useful revision info!

Journal Task

Make a new entry in the Journal section of the booklet.

Did you discover anything new today? Do you have questions in your mind that you want answers to?

Make these notes in your journal.

Glossary

Machine Learning

Supervised Learning

Unsupervised Learning

Labels

Training

Training data

Test data

Features

Exercises

Tags

Machine Learninglabels Supervised learning Unsupervised learning features model training data trainingtest data