Nearest neighbors method is used to determine a predefined number of data points that are closer to a sample point and predict its label. sklearn.neighbors provides utilities for unsupervised and supervised neighbors-based learning methods.
KNeighborsClassifier classifies based on k nearest neighbors of every query point, where k is an integer value specified by the user. RadiusNeighborsClassifier classifies based on the number of neighbors present in a fixed radius r of every training point.
KNeighborsRegressor predicts based on the k nearest neighbors of each query point. RadiusNeighborsRegressor predicts based on the neighbors present in a fixed radius r of the query point.
The following code snippet illustrates importing required modules and loading cancer dataset. import sklearn.datasets as datasets from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier cancer = datasets.load_breast_cancer() # Loading the data set
The following code creates training and test data sets, initializes a KNN classifier, and fits it with training data.
X_train, X_test, Y_train, Y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target, random_state=42)
knn_classifier = KNeighborsClassifier()
knn_classifier = knn_classifier.fit(X_train, Y_train)
The following code determines the accuracy of model on train and test data sets. print('Accuracy of Train Data :', knn_classifier.score(X_train,Y_train)) print('Accuracy of Test Data :', knn_classifier.score(X_test,Y_test))