Beginner

Z-score Standardization

Beginner

~10 min

code completion

Z-score Standardization

Z-score standardization (also called standard scaling) transforms features to have zero mean and unit variance:

X_{std} = \frac{X - μ}{σ}

where $μ = mean (X)$ and $σ = std (X)$ .

Unlike min-max normalization, standardization is robust to outliers and is preferred for algorithms that assume normally distributed data (e.g., linear regression, PCA).

Your task:

Implement z_score_standardize(X). Assume the standard deviation is nonzero.

Example Tests

Symmetric array: mean=0, std=2

Input: {"X":[-3,-1,0,1,3]}

Expected: [-1.5,-0.5,0,0.5,1.5]

Sum of output is 0 (zero mean)

Input: {"X":[2,4,6,8,10]}

Expected: 0

3-element array: known z-scores

Input: {"X":[10,20,30]}

Expected: [-1.22474,0,1.22474]

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