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Standardization centers data at the mean ($\mu$), so values below the mean become negative. Min-max normalization shifts and scales data so the minimum becomes 0 and the maximum becomes 1, keeping all values non-negative within [0, 1].

Conditions: Standardization uses $z = \frac{x - \mu}{\sigma}$.; Min-max uses $X_{\text{norm}} = \frac{X - X_{\min}}{X_{\max} - X_{\min}}$.

Standardization centers data at the mean ($\mu$), so values below the mean become negative. Min-max normalization shifts and scales data so the minimum becomes 0 and the maximum becomes 1, keeping all values non-negative within [0, 1].

Conditions: Standardization uses $z = \frac{x - \mu}{\sigma}$.; Min-max uses $X_{\text{norm}} = \frac{X - X_{\min}}{X_{\max} - X_{\min}}$.

Feature scaling is important primarily to achieve faster convergence in optimization algorithms like gradient descent, and to ensure that distance-based algorithms compute distances appropriately without larger-scale features dominating.

Conditions: Applies to iterative optimization algorithms like gradient descent.; Applies to distance-based machine learning algorithms like KNN or K-means.

Feature scaling is important primarily to achieve faster convergence in optimization algorithms like gradient descent, and to ensure that distance-based algorithms compute distances appropriately without larger-scale features dominating.

Conditions: Applies to iterative optimization algorithms like gradient descent.; Applies to distance-based machine learning algorithms like KNN or K-means.

No, standardization does not change the general shape of its distribution; it only shifts the mean to zero and scales the standard deviation to one. It is a linear transformation, so a highly skewed distribution remains skewed after standardization.

Conditions: The dataset has a well-defined mean and standard deviation.; Standardization is applied as a linear transformation.

No, standardization does not change the general shape of its distribution; it only shifts the mean to zero and scales the standard deviation to one. It is a linear transformation, so a highly skewed distribution remains skewed after standardization.

Conditions: The dataset has a well-defined mean and standard deviation.; Standardization is applied as a linear transformation.