如下所示:
from sklearn.datasets import load_iris
iris = load_iris()
print iris.data.shape
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size = 0.25, random_state = 33)
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
knc = KNeighborsClassifier()
knc.fit(X_train, y_train)
y_predict = knc.predict(X_test)
print 'The accuracy of K-Nearest Neighbor Classifier is: ', knc.score(X_test, y_test)
from sklearn.metrics import classification_report
print classification_report(y_test, y_predict, target_names = iris.target_names)
以上这篇在python中利用KNN实现对iris进行分类的方法就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持亿速云。
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