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Min-max normalization is a linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).

Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).

Min-max normalization is a linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).

Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).

Min-max normalization transforms a feature value $X$ into $X_{\text{norm}} = \frac{X - X_{\min}}{X_{\max} - X_{\min}}$, mapping the feature's observed range onto the interval $[0, 1]$.

Conditions: Applied per numerical feature.; Requires a finite minimum $X_{\min}$ and maximum $X_{\max}$ for the feature.; Assumes $X_{\max} \neq X_{min}$ to avoid division by zero.

Min-max normalization transforms a feature value $X$ into $X_{\text{norm}} = \frac{X - X_{\min}}{X_{\max} - X_{\min}}$, mapping the feature's observed range onto the interval $[0, 1]$.

Conditions: Applied per numerical feature.; Requires a finite minimum $X_{\min}$ and maximum $X_{\max}$ for the feature.; Assumes $X_{\max} \neq X_{min}$ to avoid division by zero.

Standardization transforms a feature value $x$ into $z = \frac{x - \mu}{\sigma}$, targeting a mean of 0 and a standard deviation of 1 for the transformed feature.

Conditions: Applied per numerical feature.; Uses the feature mean $\mu$ and standard deviation $\sigma$.; Assumes $\sigma > 0$.

Standardization transforms a feature value $x$ into $z = \frac{x - \mu}{\sigma}$, targeting a mean of 0 and a standard deviation of 1 for the transformed feature.

Conditions: Applied per numerical feature.; Uses the feature mean $\mu$ and standard deviation $\sigma$.; Assumes $\sigma > 0$.

Standardization (or Z-score normalization) is a linear transformation that centers the data around zero and scales it to have a standard deviation of one. It subtracts the mean of the feature from each data point and divides by the standard deviation.

Conditions: Applied to a continuous numerical feature.; Requires knowing the mean and standard deviation of the feature.; The standard deviation must not be zero (to avoid division by zero).

Standardization (or Z-score normalization) is a linear transformation that centers the data around zero and scales it to have a standard deviation of one. It subtracts the mean of the feature from each data point and divides by the standard deviation.

Conditions: Applied to a continuous numerical feature.; Requires knowing the mean and standard deviation of the feature.; The standard deviation must not be zero (to avoid division by zero).