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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What type of data is Logistic Regression suitable for?
💡 Hint: Think about scenarios with only two possible outcomes.
Question 2
Easy
Name one advantage of Decision Trees.
💡 Hint: Consider how visual representations help understand results.
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 algorithm is primarily used for binary classification?
- Logistic Regression
- K-Means Clustering
- Linear Regression
💡 Hint: Remember the name suggests its purpose.
Question 2
True or False: Decision Trees can only handle linear data.
- True
- False
💡 Hint: Think about how trees split data.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a dataset with features that do not follow a linear pattern. Explain which classification algorithm (Logistic Regression, Decision Trees, or KNN) would be most appropriate and why.
💡 Hint: Think about the characteristics each algorithm requires in the data.
Question 2
Design a scenario where KNN might fail dramatically compared to Logistic Regression. Describe the scenario and why KNN would struggle.
💡 Hint: Evaluate the impact of 'curse of dimensionality'.
Challenge and get performance evaluation