Exit 25 / 40

Question 25

Unclassified

Standardization (z-score) transforms features to have:

A. Min 0 and max 1 B. Mean 0 and standard deviation 1 C. Sum 1 D. Median 0 and IQR 1

Correct answer: B

Explanation

Standardization, or z-score scaling, uses the formula z = (x - μ) / σ, which subtracts the mean and divides by the standard deviation. This transformation centers the data at 0 and rescales it so the spread is 1, so the features have "mean 0 and standard deviation 1."

Why each option is right or wrong

A. Min 0 and max 1

B. Mean 0 and standard deviation 1

The z-score transformation is defined as z = (x - μ) / σ, where μ is the feature mean and σ is the feature standard deviation. Subtracting μ recenters the distribution at 0, and dividing by σ rescales the dispersion so the transformed variable has unit variance, i.e., standard deviation 1.

C. Sum 1

D. Median 0 and IQR 1