# How does Matlab implement kNN algorithm?

## How does Matlab implement kNN algorithm?

Mdl = fitcknn( Tbl , Y ) returns a k-nearest neighbor classification model based on the predictor variables in the table Tbl and response array Y . Mdl = fitcknn( X , Y ) returns a k-nearest neighbor classification model based on the predictor data X and response Y .

What is KNN algorithm example?

With the help of KNN algorithms, we can classify a potential voter into various classes like “Will Vote”, “Will not Vote”, “Will Vote to Party ‘Congress’, “Will Vote to Party ‘BJP’. Other areas in which KNN algorithm can be used are Speech Recognition, Handwriting Detection, Image Recognition and Video Recognition.

How do you make a predictive model in MATLAB?

The steps are:

1. Clean the data by removing outliers and treating missing data.
2. Identify a parametric or nonparametric predictive modeling approach to use.
3. Preprocess the data into a form suitable for the chosen modeling algorithm.
4. Specify a subset of the data to be used for training the model.

### How do you use linear regression to predict in MATLAB?

Description

1. example. ypred = predict( mdl , Xnew ) returns the predicted response values of the linear regression model mdl to the points in Xnew .
2. [ ypred , yci ] = predict( mdl , Xnew ) also returns confidence intervals for the responses at Xnew .
3. example.

How does k-nearest neighbors algorithm work?

KNN works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression).

What is K in the KNN algorithm?

‘k’ in KNN is a parameter that refers to the number of nearest neighbours to include in the majority of the voting process.

## What would be the steps for a 5 nearest neighbor classification algorithm?

Breaking it Down – Pseudo Code of KNN

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