Practice Visualizing Decision Boundaries (optional For 2d Data) (6) - Classification Algorithms

Practice: Visualizing Decision Boundaries (Optional for 2D Data)

Lesson 6Classification Algorithms

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9 questions

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2 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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3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is a decision boundary?
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Think of how a fence separates different areas.
Question 2.
Name a library used for visualizing data in Python.
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It's commonly used for creating plots and graphs.
3Question 3.
What defines a decision boundary?
- A boundary that indicates where a feature lies
- A line that separates different classes
- A point representing a class
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Think about how different areas might represent different classifications.
4Question 4.
True or False: Decision boundaries only apply to linear classification models.
- True
- False
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Think about how complex classes can be.
Question 5.
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.
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Consider using np.meshgrid for creating your grid in the feature space.
Question 6.
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.
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Explore the impact of dimensionality or alternate classifiers.

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