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KNN Imputer#

An unsupervised imputer that replaces missing values in a dataset with the distance-weighted average of the samples' k nearest neighbors' values. The average for a continuous feature column is defined as the mean of the values of each donor. Similarly, average is defined as the most frequent value for categorical features.

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\KNNImputer;
use Rubix\ML\Graph\Trees\BallTee;
use Rubix\ML\Kernels\Distance\SafeEuclidean;

$transformer = new KNNImputer(10, false, '?', new BallTree(30, new SafeEuclidean()));

Additional Methods#

This transformer does not have any additional methods.

References#

  1. O. Troyanskaya et al. (2001). Missing value estimation methods for DNA microarrays. ↩