[source] # Hot Deck Imputer A *hot deck* is a set of complete donor samples that may be referenced when imputing a value for a missing feature value. Hot Deck Imputer first finds the k most similar donors to a sample that contains a missing value and then chooses a value at random from those donors. !!! note Requires a NaN safe distance kernel such as [Safe Euclidean](../kernels/distance/safe-euclidean.md) for continuous features. **Interfaces:** [Transformer](api.md#transformers), [Stateful](api.md#stateful), [Persistable](../persistable.md) **Data Type Compatibility:** Depends on distance kernel ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | k | 5 | int | The number of nearest neighbor donors to consider when imputing a value. | | 2 | weighted | false | bool | Should we use distances as weights when selecting a donor sample? | | 3 | categoricalPlaceholder | '?' | string | The categorical placeholder denoting the category that contains missing values. | | 4 | tree | BallTree | Spatial | The spatial tree used to run nearest neighbor searches. | ## Example ```php use Rubix\ML\Transformers\HotDeckImputer; use Rubix\ML\Graph\Trees\BallTree; use Rubix\ML\Kernels\Distance\Gower; $transformer = new HotDeckImputer(20, false, '?', new BallTree(50, new Gower(1.0))); ``` ## Additional Methods This transformer does not have any additional methods. ## References [^1]: C. Hasler et al. (2015). Balanced k-Nearest Neighbor Imputation.