K-Dimensional Tree K Nearest Neighbours (KDTreeKNN)

Description

KD tree KNN is an extension to the KNN algorithm. It organizes data into KD trees (a special data type in computer science) where the data closest together make up a branch of the tree and data farther apart are correspondingly farther apart on the tree. This data organization technique allows KNN to skip calculations in branches of the tree that are far away from the unknown data point at the cost of some computations required to build the tree during training.

Parameters

same as KNN

ParameterDescription
k number of nearest neighbours to take into account when making a classification

MLGenius will help you determine the best k to use for your dataset

Additional Information

Just like in KNN, you may also customize the distance function in this algorithm. Please contact MLGenius if you need assistance in doing that.

Pros and Cons

K-dimensional tree with k-nearest neighbour machine learning algorithm is generally great when the number of dimensions in your dataset isn’t too big (ie. <10). See KNN for more info.