在python中利用KNN实现对iris进行分类的方法

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.

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)

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