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""" Nearest neighbor classification with scikit-learn full details at: http://scikit-learn.org/stable/modules/neighbors.html#classification """ import numpy as np from sklearn import neighbors, datasets import decision_boundary # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # take the first two features. y = iris.target # the parameters of the scikit-learn nearest neighbor # classifier: # sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, # weights='uniform', algorithm='auto', leaf_size=30, p=2, # metric='minkowski') # weights refers to how to weight each example # 'algorithm' is the choice of algorithm for storing the # training data ('brute', 'ball_tree', 'kd-tree') # complete description of the available metrics: # http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html#sklearn.neighbors.DistanceMetric classifier = neighbors.KNeighborsClassifier(n_neighbors=10) decision_boundary.plot_boundary(classifier, X, y)