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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is feature scaling?
💡 Hint: Think about why it's important for model performance.
Question 2
Easy
What does standardization do to a dataset?
💡 Hint: Recall the formulas associated with 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 primary purpose of feature scaling?
- To increase model size
- To ensure features contribute equally
- To reduce accuracy
💡 Hint: Consider why your model might get confused by vastly different scales.
Question 2
True or False: Normalization is always the preferred scaling method when applying machine learning algorithms.
- True
- False
💡 Hint: Reflect on the scenarios in which normalization is useful versus when standardization may be better.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with multiple features with very different scales (e.g., age, income, and height), describe how you would preprocess the dataset for a K-NN algorithm.
💡 Hint: Consider which scaling method would best suit the feature distributions.
Question 2
Suppose you have outliers in your data's salary feature while using a machine learning model that relies on distance metrics. How would this affect your scaling and modeling choices?
💡 Hint: Reflect on how outliers interact with different scaling methods.
Challenge and get performance evaluation