Practice Advantages (3.6.4) - Kernel & Non-Parametric Methods - Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What advantage do decision trees have in terms of interpretability?

💡 Hint: Think about how you can trace back the model's decisions.

Question 2 Easy

Can decision trees handle both numerical and categorical data?

💡 Hint: Consider what types of features you find in datasets.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

Why are decision trees considered interpretable?

They use only linear models.
They visualize decision-making paths clearly.
They require extensive preprocessing.
They cannot handle categorical data.

💡 Hint: Look for clarity in how decisions are represented.

Question 2

Are decision trees capable of handling mixed data types?

True
False

💡 Hint: Consider the structure of cultural data.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with both numerical and categorical features, outline the steps you would take to prepare it for analysis using a decision tree.

💡 Hint: Consider the common preprocessing methods you learned.

Challenge 2 Hard

Critically analyze how the flexibility in handling mixed data types affects the usability of decision trees in a specific industry.

💡 Hint: Think about the types of data typically found in healthcare datasets.

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