Editing Distance matrix (section)

==== K-Nearest Neighbors ====
A distance matrix is utilized in the [[k-NN algorithm]] which is one of the slowest but simplest and most used instance-based machine learning algorithms that can be used both in classification and regression tasks. It is one of the slowest machine learning algorithms since each test sample's predicted result requires a fully computed distance matrix between the test sample and each training sample in the training set. Once the distance matrix is computed, the algorithm selects the K number of training samples that are the closest to the test sample to predict the test sample's result based on the selected set's majority (classification) or average (regression) value.

* Prediction time complexity is <math>O(k * n * d)</math>, to compute the distance between each test sample with every training sample to construct the distance matrix where:

# k = number of nearest neighbors selected 
# n = size of the training set
# d = number of dimensions being used for the data
This classification focused model predicts the label of the target based on the distance matrix between the target and each of the training samples to determine the K-number of samples that are the closest/nearest to the target.
{{Photo montage|
| photo1a =DistanceMatrix_KNN.png{{!}}The distance matrix used to select K train samples for K-nn
| photo1b =K_nearestNeighborVisual.png{{!}}Machine Learning model predicting target value with K-NN
| size = 650
| border = 0
| color = transparent
}}