[source] # Z Scale Standardizer A method of centering and scaling a dataset such that it has 0 mean and unit variance, also known as a Z-Score. Although Z-Scores are technically unbounded, in practice they mostly fall between -3 and 3 - that is, they are no more than 3 standard deviations away from the mean. $$ {\displaystyle z = {x - \mu \over \sigma }} $$ **Interfaces:** [Transformer](api.md#transformer), [Stateful](api.md#stateful), [Elastic](api.md#elastic), [Reversible](api.md#reversible), [Persistable](../persistable.md) **Data Type Compatibility:** Continuous ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | center | true | bool | Should we center the data at 0? | ## Example ```php use Rubix\ML\Transformers\ZScaleStandardizer; $transformer = new ZScaleStandardizer(true); ``` ## Additional Methods Return the means calculated by fitting the training set: ```php public means() : array ``` Return the variances calculated during fitting: ```php public variances() : array ``` ## References [^1]: T. F. Chan et al. (1979). Updating Formulae and a Pairwise Algorithm for Computing Sample Variances.