利用SVC(Support Vector Classifier)對digits數據進行分類

from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report


digits = load_digits()#載入數據
X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,test_size=0.25,random_state=33)#劃分樣本75%爲訓練數據25%爲測試數據
print(y_train.shape)
print(y_test.shape)


#對訓練數據和測試數據進行標準化
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)


#進行初始化線性假設的支持向量分類器LinearSVC
lsvc =LinearSVC()
lsvc.fit(X_train,y_train)
y_predict = lsvc.predict(X_test) 
 #使用模型自帶的評估函數進行準確性預測
print('The Accurancy of Linear SVC is',lsvc.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=digits.target_names.astype(str)))