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What is One-Hot Encoding?
What is the purpose of One-Hot Encoding?
What does each category value get transformed into during One-Hot Encoding?
Why is One-Hot Encoding important in data science?
What is the downside of One-Hot Encoding in terms of dimensionality?
Which technique can be used to reduce dimensionality in One-Hot Encoding?
In which real-world scenario can One-Hot Encoding be used?
How does One-Hot Encoding interact with tree-based algorithms?
What is a caution to consider when using One-Hot Encoding?
Which is an alternative to One-Hot Encoding for handling categorical variables?
Which encoding method would be more efficient in handling a categorical variable with thousands of unique categories?
What is the main advantage of One-Hot Encoding over Label Encoding?
What is a potential issue when applying One-Hot Encoding to a dataset with a very high number of categories?
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