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KNN (K- Nearest Neighbors)It can be used for both classification and regression problems. However, it is more widely used in classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors. The case being assigned to the class is most common amongst its K nearest neighbors measured by a distance function.
These distance functions can be Euclidean, Manhattan, Minkowski and Hamming distance. First three functions are used for continuous function and fourth one (Hamming) for categorical variables. If K = 1, then the case is simply assigned to the class of its nearest neighbor. At times, choosing K turns out to be a challenge while performing KNN modeling.
More: Introduction to k-nearest neighbors : Simplified.
KNN can easily be mapped to our real lives. If you want to learn about a person, of whom you have no information, you might like to find out about his close friends and the circles he moves in and gain access to his/her information!
Things to consider before selecting KNN:
- KNN is computationally expensive
- Variables should be normalized else higher range variables can bias it
- Works on pre-processing stage more before going for KNN like outlier, noise removal
- #Import Library
- from sklearn.neighbors import KNeighborsClassifier
- #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
- # Create KNeighbors classifier object model
- KNeighborsClassifier(n_neighbors=6) # default value for n_neighbors is 5
- # Train the model using the training sets and check score
- model.fit(X, y)
- #Predict Output
- predicted= model.predict(x_test)
R Codelibrary(knn)
- x <- cbind(x_train,y_train)
- # Fitting model
- fit <-knn(y_train ~ ., data = x,k=5)
- summary(fit)
- #Predict Output
- predicted= predict(fit,x_test)
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