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The Z-score formula normalizes the data by subtracting the mean ($\mu$) from the original value ($X$) and dividing the result by the standard deviation ($\sigma$). The formula is $X' = \frac{X - \mu}{\sigma}$.

Conditions: Requires knowing the mean and standard deviation of the dataset

The Z-score formula normalizes the data by subtracting the mean ($\mu$) from the original value ($X$) and dividing the result by the standard deviation ($\sigma$). The formula is $X' = \frac{X - \mu}{\sigma}$.

Conditions: Requires knowing the mean and standard deviation of the dataset

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).