Practice Building A Decision Tree: The Splitting Process (5.2) - Supervised Learning - Classification Fundamentals (Weeks 6)
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Building a Decision Tree: The Splitting Process

Practice - Building a Decision Tree: The Splitting Process

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the purpose of the splitting process in decision trees?

💡 Hint: Consider why we want data to be pure.

Question 2 Easy

Define Gini impurity in your own words.

💡 Hint: Think about how probability relates to class labels.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main goal of the splitting process in a decision tree?

To increase tree depth
To enhance data purity
To reduce overall data size

💡 Hint: Remember the primary focus of each node in a decision tree.

Question 2

True or False: Gini impurity and Entropy both measure node purity.

True
False

💡 Hint: Think about how both contribute to understanding node quality.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a decision tree for predicting whether a student will pass a course based on hours studied, attendance, and previous grades. Include key impurity measures and pruning techniques.

💡 Hint: Think about each feature's influence on passing rates.

Challenge 2 Hard

Evaluate the potential of using decision tree models in healthcare classifications. Discuss how pruning may impact such models.

💡 Hint: Reflect on how patient data nuances can affect predictions and the need for model clarity.

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Reference links

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