Visit Tutorials below:
KNN Numerical Example (hand computation)
Here is step by step on how to compute K-nearest neighbors KNN algorithm:
We will use again the previous example to calculate KNN by hand computation. If you want to download the MS excel companion of this tutorial, click here
We have data from the questionnaires survey (to ask people opinion) and objective testing with two attributes (acid durability and strength) to classify whether a special paper tissue is good or not. Here is four training samples
Now the factory produces a new paper tissue that pass laboratory test with X1 = 3 and X2 = 7. Without another expensive survey, can we guess what the classification of this new tissue is?
1. Determine parameter K = number of nearest neighbors
Suppose use K = 3
2. Calculate the distance between the query-instance and all the training samples
Coordinate of query instance is (3, 7), instead of calculating the distance we compute square distance which is faster to calculate (without square root)
3. Sort the distance and determine nearest neighbors based on the K-th minimum distance
4. Gather the category of the nearest neighbors. Notice in the second row last column that the category of nearest neighbor (Y) is not included because the rank of this data is more than 3 (=K).
5. Use simple majority of the category of nearest neighbors as the prediction value of the query instance
We have 2 good and 1 bad, since 2>1 then we conclude that a new paper tissue that pass laboratory test with X1 = 3 and X2 = 7 is included in Good category.
Preferable reference for this tutorial is
Teknomo, Kardi. K-Nearest Neighbors Tutorial. http:\\people.revoledu.com\kardi\ tutorial\KNN\