How does KNN Works?

Data Science, AI and ML
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#1

The K-NN working can be explained on the basis of the below algorithm:

  • Step-1: Select the number K of the neighbors
  • Step-2: Calculate the Euclidean distance of K number of neighbors
  • Step-3: Take the K nearest neighbors as per the calculated Euclidean distance.
  • Step-4: Among these k neighbors, count the number of the data points in each category.
  • Step-5: Assign the new data points to that category for which the number of the neighbor is maximum.
  • Step-6: Our model is ready.

Suppose we have a new data point and we need to put it in the required category. Consider the below image:

K-Nearest Neighbor(KNN) Algorithm for Machine Learning

  • Firstly, we will choose the number of neighbors, so we will choose the k=5.
  • Next, we will calculate the Euclidean distance between the data points. The Euclidean distance is the distance between two points, which we have already studied in geometry. It can be calculated as:

K-Nearest Neighbor(KNN) Algorithm for Machine Learning

  • By calculating the Euclidean distance we got the nearest neighbors, as three nearest neighbors in category A and two nearest neighbors in category B. Consider the below image:

K-Nearest Neighbor(KNN) Algorithm for Machine Learning

  • As we can see the 3 nearest neighbors are from category A, hence this new data point must belong to category A.