Practice - Feature Scaling
Practice Questions
Test your understanding with targeted questions
What is feature scaling?
💡 Hint: Think about why it's important for model performance.
What does standardization do to a dataset?
💡 Hint: Recall the formulas associated with it.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary purpose of feature scaling?
💡 Hint: Consider why your model might get confused by vastly different scales.
True or False: Normalization is always the preferred scaling method when applying machine learning algorithms.
💡 Hint: Reflect on the scenarios in which normalization is useful versus when standardization may be better.
1 more question available
Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.