## What is knnsearch in matlab?

Idx = knnsearch( X , Y ) finds the nearest neighbor in X for each query point in Y and returns the indices of the nearest neighbors in Idx , a column vector. For example, you can specify the number of nearest neighbors to search for and the distance metric used in the search.

## How to use knnsearch?

Breaking it Down – Pseudo Code of KNN

- Calculate the distance between test data and each row of training data.
- Sort the calculated distances in ascending order based on distance values.
- Get top k rows from the sorted array.
- Get the most frequent class of these rows.
- Return the predicted class.

**What is Knn search?**

k-nearest neighbor search identifies the top k nearest neighbors to the query. This technique is commonly used in predictive analytics to estimate or classify a point based on the consensus of its neighbors. k-nearest neighbor graphs are graphs in which every point is connected to its k nearest neighbors.

### How do kd trees work?

A K-D Tree(also called as K-Dimensional Tree) is a binary search tree where data in each node is a K-Dimensional point in space. In short, it is a space partitioning(details below) data structure for organizing points in a K-Dimensional space.

### What is the purpose of KNN?

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems.

**What is Knn used for?**

#### How do I find my nearest Neighbour?

Here is step by step on how to compute K-nearest neighbors KNN algorithm:

- Determine parameter K = number of nearest neighbors.
- Calculate the distance between the query-instance and all the training samples.
- Sort the distance and determine nearest neighbors based on the K-th minimum distance.