The document summarizes the K-nearest neighbor (KNN) algorithm. KNN is a memory-based algorithm that finds the K training samples nearest to a query point and predicts the query's classification based on the majority classification of its neighbors. The summary explains: 1) KNN measures the distance between query points and training samples to classify new objects based on the majority category of its K nearest neighbors. 2) To make a prediction, KNN determines K, calculates distances between the query and all training points, identifies the K nearest neighbors, collects their classifications, and predicts the query's classification based on the majority of its neighbors. 3) An example is given where KNN predicts the winner of an

1. Nearest Neighbor AlgorithmZaffar Ahmed Shaikh

2. TopicsIntroduction – Memory-based algorithmsK-nearest neighbor (KNN) algorithmHow KNN works?KNN ExampleDifferent types of KNN

3. IntroductionMemory-based algorithms utilize the entire user-item database to generate a prediction. They find a set of users, known as neighbors, that have a history of agreeing with the target user. Once a neighborhood of users is formed, the preferences of neighbors are combined to produce a prediction or top-K recommendation for the active user. K-nearest neighbor (KNN)The nearest neighbor algorithm measures the distance dE(Xi,Xj)between query points Xi and a set of training samples Xjto classify a new object based on majority of K-nearest neighbor category of Y attributes of training samples. Query point Xi = x1, x2, x3, ……….., xnTraining Sample Xj= x1, x2, x3, ……….., xn

4. How KNN works?Determine K (no of nearest neighbors)Calculate distance (Euclidean, Manhattan)Determine K-minimum distance neighborsGather category Y values of nearest neighbors Use simple majority of nearest neighbors to predict value of query instance

5. KNN ExamplePredict who will win today’s Cricket match between India and Pakistan based on users rating and previous results of matches played between the two teams.

6. 1. Determine KDetermine value of K Suppose K = 32. Calculate distanceCoordinates of query instance are (4,3,3)Coordinates of training instance(1) are (7,2,1)D = SQRT ((7-4)2+(2-3) 2+(1-3) 2) = 3.74165

7. 2. Calculate distance

8. 3. Determine K-minimum distance neighborsK = 3

9. 4. Gather category Y values of nearest neighbors

10. 5. Use simple majority of nearest neighbors to predict value of query instanceHere India has won 2 matches 2 (-) signs and Pakistan has won 1 match 1 (+) signWe conclude that India will win today’s match

11. Different types of KNN KNN for ClassificationKNN for PredictionKNN for Smoothing

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