5.9 - Hyperparameter Tuning
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What is a hyperparameter?
💡 Hint: Think about settings used prior to training.
Name one technique for hyperparameter tuning.
💡 Hint: Consider common approaches for optimizing model parameters.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the purpose of hyperparameter tuning?
💡 Hint: Think about what we are trying to achieve with tuning.
True or False: Grid Search only samples a subset of hyperparameter combinations.
💡 Hint: Recall how Grid Search operates.
1 more question available
Challenge Problems
Push your limits with advanced challenges
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.
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.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.