Practice Visualizing Decision Boundaries (Optional for 2D Data) - 6 | Classification Algorithms | Data Science Basic
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Knowledge

6 - Visualizing Decision Boundaries (Optional for 2D Data)

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a decision boundary?

💡 Hint: Think of how a fence separates different areas.

Question 2

Easy

Name a library used for visualizing data in Python.

💡 Hint: It's commonly used for creating plots and graphs.

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 defines a decision boundary?

  • A boundary that indicates where a feature lies
  • A line that separates different classes
  • A point representing a class

💡 Hint: Think about how different areas might represent different classifications.

Question 2

True or False: Decision boundaries only apply to linear classification models.

  • True
  • False

💡 Hint: Think about how complex classes can be.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset with three overlapping classes. Write a Python function to visualize the decision boundaries for a KNN classifier. Include how you would explain the decision boundaries to a novice.

💡 Hint: Consider using np.meshgrid for creating your grid in the feature space.

Question 2

You notice that some predictions are failing. Describe what you could change in your classifier or dataset to address the overlapping classes and improve decision boundary execution.

💡 Hint: Explore the impact of dimensionality or alternate classifiers.

Challenge and get performance evaluation