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脚本之家。
