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ASSESSMENT TASKS

 

Task 1 FORMATIVE TASK Decision Trees in Principle

 

FORMATIVE TASK

Instruction: Produce a briefing document for your boss to explain Decision Trees and their potential uses in your organisation. The report must contain the following:

• An outline of what is understood by a decision tree, defining the terms ‘root node’, ‘parent nodes’, ‘child nodes’, ‘edges’ ad ‘leaf nodes’.
• Identify and explain the use-cases of decision tree models
• Discuss the advantages and disadvantages of decision trees, and how they might help promote the business of the organisation

Task 2 SUMMATIVE TASK Decision Trees in your Organisation

 

SUMMATIVE TASK

Instruction: Build a decision tree model for use in your organisation, and write a report on it. Your report must contain the following:

• An outline of how Python was used to create a decision tree model that is appropriate for the dataset in question, including decisions about splitting, pruning and Entropy (LO 2.1, 3.1, 4.1)
• Identify and explain how Information Gain was calculated, and how the ID3 algorithm was applied to the dataset (LO 2.2, 2.1, 3.2)
• A judgment as to the conclusions that can be drawn from the data, and the accuracy of these conclusions in making future decisions, including potential revision to the ID3 Algorithm (LO 3, 2.4, 4.2)

 

Learning Outcomes: To achieve this unit, the learner must be able to:	Assessment Criteria: Assessment of these learning outcomes will require a learner to demonstrate that they can:
2. Understand how to construct a decision tree in data science.	2.1            Explain what is meant by splitting and pruning a decision tree. 2.2                                     Define: –          Entropy –          Information Gain. 2.3           Explain the key steps in the ID3 (Iterative Dichotomiser) algorithm. 2.4           Analyse improvements and extensions to the ID3 algorithm.
3. Be able to perform calculations             using decision tree metrics in data science.	3.1            Calculate correctly Entropy values for a dataset. 3.2           Calculate correctly Information Gain values for a dataset. 3.3           Create accurate visualisations of the Entropy function.
4. Be able to build a decision tree model in data science.	4.1           Use Python to build a decision tree model that is appropriate for a given dataset. 4.2           Use Python to create visualisations that are appropriate for a decision tree.