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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 nearest donors to a sample that contains a missing value and then chooses a value at random from the neighborhood.

Note

Requires a NaN safe distance kernel such as Safe Euclidean for continuous features.

Interfaces: Transformer, Stateful, Persistable

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

use Rubix\ML\Transformers\HotDeckImputer;
use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Kernels\Distance\SafeEuclidean;

$transformer = new HotDeckImputer(20, false, '?', new BallTree(50, new SafeEuclidean()));

Additional Methods

This transformer does not have any additional methods.

References

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1. C. Hasler et al. (2015). Balanced k-Nearest Neighbor Imputation. ↩

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Last update: 2021-04-11