In [2]:

    

from sklearn.datasets import load_digits
digits=load_digits()
digits.data.shape


    

    
Out[2]:





(1797, 64)

In [3]:

    

from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,test_size=0.25,random_state=33)
y_train.shape


    

    
Out[3]:





(1347,)

In [4]:

    

y_test.shape


    

    
Out[4]:





(450,)

In [5]:

    

from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
ss=StandardScaler()
X_train=ss.fit_transform(X_train)
X_test=ss.transform(X_test)
lsvc=LinearSVC()
lsvc.fit(X_train,y_train)
y_predict=lsvc.predict(X_test)


    

In [6]:

    

print 'Accuracy of LinearSVC is ', lsvc.score(X_test,y_test)


    

    




Accuracy of LinearSVC is  0.953333333333

In [7]:

    

from sklearn.metrics import classification_report
print classification_report(y_test,y_predict,target_names=digits.target_names.astype(str))


    

    




             precision    recall  f1-score   support

          0       0.92      1.00      0.96        35
          1       0.96      0.98      0.97        54
          2       0.98      1.00      0.99        44
          3       0.93      0.93      0.93        46
          4       0.97      1.00      0.99        35
          5       0.94      0.94      0.94        48
          6       0.96      0.98      0.97        51
          7       0.92      1.00      0.96        35
          8       0.98      0.84      0.91        58
          9       0.95      0.91      0.93        44

avg / total       0.95      0.95      0.95       450

In [ ]: