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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is modeling in data science?
💡 Hint: Think of how predictions are made from data.
Question 2
Easy
Name one type of machine learning algorithm.
💡 Hint: Consider what algorithms are used for predicting outcomes.
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 does modeling involve?
- Creating predictive models
- Collecting data
- Cleaning data
💡 Hint: Think about which phase directly uses algorithms.
Question 2
True or False: Overfitting indicates a model is generalizing well to new data.
- True
- False
💡 Hint: Think about what happens when a model focuses too much on the training set.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have trained a model, but your validation accuracy is significantly lower than training accuracy. What steps could you take to address this issue?
💡 Hint: Think about how you could create a more flexible or generalized model.
Question 2
Create a balanced dataset for training a classification model. How would you approach this, and what techniques might you use?
💡 Hint: Consider ways to adjust your data rather than just throwing out data.
Challenge and get performance evaluation