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What is the purpose of scaling in data science?
What does normalization aim to achieve?
Which method assigns a score of 0 to the smallest value and 1 to the largest value in the dataset?
Which method is also known as Z-score Normalization?
Which method is resistant to outliers by using the median and interquartile range?
Which method scales data based on the absolute maximum value?
Which method gives a score based on the rank of each value?
When is scaling and normalization most useful in data science?
What should you consider when choosing the right method of scaling and normalization?
What is an implication of scaling and normalization on machine learning models?
Which scaling method does not affect the position of zero in sparse data?
What does Quantile Normalization specifically aim to make uniform across samples?
Which scaling method would be most suitable for data with a known fixed range?
What can be a disadvantage of Standard Scaling in data with a skewed distribution?
Why is Robust Scaling preferred when dealing with datasets containing many outliers?
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