[source] # Safe Euclidean An Euclidean distance metric suitable for samples that may contain NaN (not a number) values i.e. missing data. The Safe Euclidean metric approximates the Euclidean distance function by dropping NaN values and scaling the distance according to the proportion of non-NaNs (in either a or b or both) to compensate. **Data Type Compatibility:** Continuous ## Parameters This kernel does not have any parameters. ## Example ```php use Rubix\ML\Kernels\Distance\SafeEuclidean; $kernel = new SafeEuclidean(); ``` ## References [^1]: J. K. Dixon. (1978). Pattern Recognition with Partly Missing Data.