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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 what you must set before training begins.
Question 2
Easy
What is the main disadvantage of Grid Search?
💡 Hint: Consider the time and resources required for trying many combinations.
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 are hyperparameters?
- Learned from data
- Set before training
- Unique to each model
💡 Hint: Consider if these are specified in advance or learned from the data.
Question 2
Grid Search is primarily used for which purpose?
- Data Cleaning
- Hyperparameter Tuning
- Model Evaluation
💡 Hint: Focus on its main goal within the model training process.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are working with a dataset with significantly imbalanced classes and are tasked to tune a classification model. Describe which hyperparameter tuning method you would employ and why.
💡 Hint: Think about the size and characteristics of your dataset.
Question 2
You've performed hyperparameter tuning using Grid Search and identified an optimal set of hyperparameters. However, upon evaluating the model on real-world data, performance is lacking. Discuss potential reasons and solutions.
💡 Hint: Consider issues related to model evaluation and data representation.
Challenge and get performance evaluation