Standardization is preferred when outliers are present because it adjusts variables to what properties?

Standardization is preferred when outliers are present because it adjusts variables to what properties?

• A. Mean Zero and Variance One
• B. Fixed Range Between Zero and One
• C. Changes The Shape Of Distribution
• D. Reduces Dimensionality

Answer: (A)

Standardization, or z-score normalization, transforms each feature by subtracting its mean and dividing by its standard deviation. This centers the data around zero and scales it so the spread is one, meaning each variable ends up with a mean of zero and a variance (or standard deviation) of one. Doing this puts different features on the same scale, so no single variable dominates models that rely on distances or gradient-based learning. This approach is particularly helpful when outliers are present because it uses the data’s spread to set the scale rather than a fixed range, preserving relative differences in the bulk of the data while still accommodating extreme values. It does not produce a fixed [0,1] range, nor does it change distribution shape or reduce dimensionality.

Standardization, or z-score normalization, transforms each feature by subtracting its mean and dividing by its standard deviation. This centers the data around zero and scales it so the spread is one, meaning each variable ends up with a mean of zero and a variance (or standard deviation) of one. Doing this puts different features on the same scale, so no single variable dominates models that rely on distances or gradient-based learning. This approach is particularly helpful when outliers are present because it uses the data’s spread to set the scale rather than a fixed range, preserving relative differences in the bulk of the data while still accommodating extreme values. It does not produce a fixed [0,1] range, nor does it change distribution shape or reduce dimensionality.