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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is feature scaling and why is it important?
💡 Hint: Think about distance calculations in algorithms like KNN.
Question 2
Easy
Describe one method for handling missing values in a dataset.
💡 Hint: Consider how we can keep data instead of losing it.
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 main goal of data preprocessing?
- To enhance model performance by ensuring reliability
- To complicate the data structure
- To ignore missing values
- To increase dataset size
💡 Hint: Think about how preprocessing helps in building effective models.
Question 2
Feature scaling is crucial for which type of machine learning methods?
- Supervised only
- Unsupervised only
- Distance-based methods
- None of the above
💡 Hint: Consider algorithms that rely on measuring distances.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset for predicting customer churn with 2000 records where 5% are churners. How would you prepare this dataset for a classification model?
💡 Hint: Consider how best to maintain class proportions while preparing the data for modeling.
Question 2
Given a dataset containing continuous variables, discuss how you would choose between standardization and min-max scaling based on the distribution of your features.
💡 Hint: Visualize or describe your data's distribution to inform your scaling choice.
Challenge and get performance evaluation