Practice Feature Importance (understanding What Matters To The Model) (4.3.3) - Advanced Supervised Learning & Evaluation (Weeks 7)
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Feature Importance (Understanding What Matters to the Model)

Practice - Feature Importance (Understanding What Matters to the Model)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does feature importance indicate?

💡 Hint: Consider how helpful a feature is for making predictions.

Question 2 Easy

What is Gini impurity used for?

💡 Hint: Think about what happens when you split data into groups.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does feature importance measure?

The overall accuracy of the model
The contribution of each feature to model predictions
The number of trees in the forest

💡 Hint: Consider what feature importance calculates.

Question 2

True or False: Impurity measures are used to calculate feature importance.

True
False

💡 Hint: Think about how trees evaluate their splits.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset, explain how you would use feature importance scores to optimize your model.

💡 Hint: Think about the role of features in both model performance and understanding.

Challenge 2 Hard

Discuss how changes in data distribution may affect feature importance and what you would do in such scenarios.

💡 Hint: Consider features as dynamic elements that can evolve with data.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.