[source]

Random Hot Deck Imputer#

A method of imputation similiar to KNN Imputer but instead of computing a weighted average of the neighbors' features, Random Hot Deck picks a value from the neighborhood at random. This makes Random Hot Deck Imputer slightly less computationally complex while satisfying some balancing equations at the same time.

Note: NaN safe distance kernels, such as Safe Euclidean, are required for continuous features.

Interfaces: Transformer, Stateful, Elastic

Data Type Compatibility: Depends on distance kernel

Parameters#

# Param Default Type Description
1 k 5 int The number of nearest neighbors to consider when imputing a value.
2 weighted true bool Should we use the inverse distances as confidence scores when imputing values?
3 kernel Safe Euclidean object The distance kernel used to compute the distance between sample points.
4 placeholder '?' string The categorical placeholder variable denoting the category that contains missing values.

Additional Methods#

This transformer does not have any additional methods.

Example#

use Rubix\ML\Transformers\RandomHotDeckImputer;
use Rubix\ML\Kernels\Distance\SafeEuclidean;

$transformer = new KNNImputer(20, true, new SafeEuclidean(), '?');

References#

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