[source] # Gower A robust distance kernel that measures samples consisting of a mix of categorical and continuous data types while also handling missing (NaN) values. When comparing continuous data, the Gower metric is equivalent to the normalized [Manhattan](manhattan.md) distance and when comparing categorical data it is equivalent to the [Hamming](hamming.md) distance. > **Note:** The Gower metric expects all continuous variables to have a standardized range. The default range works for values that have been normalized between 0 and 1. **Data Type Compatibility:** Continuous, Categorical ## Parameters | # | Param | Default | Type | Description | |---|---|---|---|---| | 1 | range | 1.0 | float | The standardized range of the continuous feature columns. Ex. [0, 1] has a range of 1, [-1, 1] has a range of 2, and so forth. | ## Example ```php use Rubix\ML\Kernels\Distance\Gower; $kernel = new Gower(2.0); ``` ### References >- J. C. Gower. (1971). A General Coefficient of Similarity and Some of Its Properties.