利用KNC對iris數據進行分裂及預測

from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
iris = load_iris()


#iris數據集中包括：‘data’,'target_names','DESCR'
#查看數據維度
print(iris.data.shape)
print(iris.DESCR) 


X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.25,random_state=33)
ss = StandardScaler()
#對數據進行標準化
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
#利用KNC算法對數據進行擬合
knc = KNeighborsClassifier()
knc.fit(X_train,y_train)
#進行預測
y_predict = knc.predict(X_test)
#對一盒後的結果的精度和準確率照護綠和F1的值進行輸出
print('The Accuracy of K-Nearst Neighbour Classifier if ',knc.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names = iris.target_names))