Showing posts with label k-Nearest Neighbors Algorithms. Show all posts
Showing posts with label k-Nearest Neighbors Algorithms. Show all posts

WEEK 8: Write a program to implement k-Nearest Neighbors algorithm to classify the iris data set. Print both correct and wrong predictions.

 WEEK 8: Write a program to implement k-Nearest Neighbors algorithm to classify the iris data set. Print both correct and wrong predictions.

K-Nearest Neighbor Algorithm

Training algorithm:

 

·       For each training example (x, f (x)), add the example to the list training examples Classification algorithm:

o   Given a query instance xq to be classified,

§  Let x1 . . .xk denote the k instances from training examples that are nearest to xq

§  Return



Where, f(xi) function to calculate the mean value of the k nearest training examples.

 

Data Set:

Iris Plants Dataset: Dataset contains 150 instances (50 in each of three classes) 



























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