Practice Concept (5.2.1) - Supervised Learning – Advanced Algorithms - Data Science Advance

Practice: Concept

Lesson 5.2.1Supervised Learning – Advanced Algorithms

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9 questions

Flashcard drill
4 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is a hyperplane in the context of SVM?
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Think about the dimensions in which classes can be separated.
Question 2.
What is the purpose of the margin in SVM?
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Consider why maximizing distance would be useful.
3Question 3.
What does SVM stand for?
- Statistical Vector Model
- Support Vector Machine
- Supervised Vector Model
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Consider the context of the technique discussed in this session.
4Question 4.
True or false: The kernel trick is only applicable to linear data.
- True
- False
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Think about the capabilities that SVM gains through this technique.
Question 5.
Given a dataset with non-linearly separable classes, explain how you would approach classification using SVM and justify your choice of kernel type.
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Consider scenarios of dataset shapes and characteristics.
Question 6.
Design a comparative study on the efficiency of SVM with linear vs. RBF kernels on a synthetic dataset with both clear and overlapping classes. What results do you expect?
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Think about how complex boundaries formed by RBF could help in classification.

5 more questions in this section. Enroll free to practise them all.