Normalization changes the data in addition to scale by altering the shape of the data to facilitate analysis, especially when skewed.

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Multiple Choice

Normalization changes the data in addition to scale by altering the shape of the data to facilitate analysis, especially when skewed.

Explanation:
Normalization rescales each feature to a fixed range, typically 0 to 1, by applying a linear transformation such as x' = (x − min) / (max − min). This makes features with different scales directly comparable and helps distance-based and gradient-based models converge more reliably. It focuses on scale rather than changing the underlying distribution shape. Standardization centers data to have a mean of zero and scales to unit variance, which changes location and spread but not the bounds. PCA transforms data into new axes (principal components), altering the data structure rather than just scaling individual features. If skewness is a concern, a skew-reducing transformation (like a log or Box-Cox) is typically used before or alongside normalization to modify the shape of the distribution, whereas min-max normalization itself does not alter skew.

Normalization rescales each feature to a fixed range, typically 0 to 1, by applying a linear transformation such as x' = (x − min) / (max − min). This makes features with different scales directly comparable and helps distance-based and gradient-based models converge more reliably. It focuses on scale rather than changing the underlying distribution shape.

Standardization centers data to have a mean of zero and scales to unit variance, which changes location and spread but not the bounds. PCA transforms data into new axes (principal components), altering the data structure rather than just scaling individual features.

If skewness is a concern, a skew-reducing transformation (like a log or Box-Cox) is typically used before or alongside normalization to modify the shape of the distribution, whereas min-max normalization itself does not alter skew.

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