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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of data cleaning?
💡 Hint: Think about why we need accurate data for ML models.
Question 2
Easy
Define feature scaling.
💡 Hint: Consider how different ranges can affect models.
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 primary reason for applying feature scaling in machine learning?
- To improve accuracy
- To overcome bias
- To ensure features contribute equally
💡 Hint: Think about how different scales affect distance calculations.
Question 2
True or False: Imputation of missing values can reduce dataset variance.
- True
- False
💡 Hint: Consider the effects of filling in values on data distribution.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are given a dataset with 30% missing values in several important columns and a few outliers. What steps would you take to prepare the data for analysis?
💡 Hint: Focus on the impact of missing data on your analysis.
Question 2
After applying PCA to your dataset, you find that the first principal component explains 80% of the variance. What does this imply for the remaining features, and how might this guide feature selection?
💡 Hint: Evaluate how much information each remaining component retains.
Challenge and get performance evaluation