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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a hyperparameter?
💡 Hint: Think about settings used prior to training.
Question 2
Easy
Name one technique for hyperparameter tuning.
💡 Hint: Consider common approaches for optimizing model parameters.
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 purpose of hyperparameter tuning?
- To increase computation time
- To improve model accuracy
- To reduce training data
💡 Hint: Think about what we are trying to achieve with tuning.
Question 2
True or False: Grid Search only samples a subset of hyperparameter combinations.
- True
- False
💡 Hint: Recall how Grid Search operates.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are building a decision tree model to predict customer churn. Explain how you would use Grid Search to optimize your model's performance.
💡 Hint: Consider the model's structure in your tuning strategy.
Question 2
Describe a scenario where not using early stopping could lead to overfitting in a neural network. What would be the consequences?
💡 Hint: Think about the training process and how models adapt.
Challenge and get performance evaluation