In Python's scikit-learn for feature scaling in regression models, why does MinMaxScaler conceptually preserve data relationships better than standardization for bounded financial metrics?

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Computer Science · Python Programming

In Python's scikit-learn for feature scaling in regression models, why does MinMaxScaler conceptually preserve data relationships better than standardization for bounded financial metrics?

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It transforms features to a [0,1] range without altering zero-point or variance ratios

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Answer 1

It ignores outliers in scaling

Answer 2

It applies logarithmic transformations automatically

Answer 3

It transforms features to a [0,1] range without altering zero-point or variance ratios

Answer 4

It centers data around zero mean

In Python's scikit-learn for feature scaling in regression models, why does MinMaxScaler conceptually preserve data relationships better than standardization for bounded financial metrics?

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