Practice Common Kernels - 3.1.3 | 3. Kernel & Non-Parametric Methods | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a Linear Kernel?

πŸ’‘ Hint: Think about the simplest form of relationship.

Question 2

Easy

Describe the Polynomial Kernel in simple terms.

πŸ’‘ Hint: Remember the role of degree 'd'.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the formula for the Polynomial Kernel?

  • K(x,xβ€²) = xTxβ€²
  • K(x,xβ€²) = (xTxβ€² + c)d
  • K(x,xβ€²) = exp(βˆ’βˆ₯xβˆ’xβ€²βˆ₯Β² / 2σ²)

πŸ’‘ Hint: Think about how polynomial equations work.

Question 2

True or False: The RBF Kernel can only model linear relationships.

  • True
  • False

πŸ’‘ Hint: Consider its flexibility in high-dimensional spaces.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a classification problem where you choose between using a Linear Kernel and an RBF Kernel. Explain your choice and the expected outcomes.

πŸ’‘ Hint: Consider characteristics of your datasets.

Question 2

You are given a dataset that is highly dimensional and sparse. Discuss which kernel would perform best and why, also mention any risks involved.

πŸ’‘ Hint: Reflect on data sparsity effects on kernels.

Challenge and get performance evaluation