SVM Classification python example using sklearn 分类例子

1. No cross validation

ref: 

http://stackoverflow.com/questions/16927964/how-to-calculate-precision-recall-and-f-score-with-libsvm-in-python

from sklearn import svm from sklearn import metrics from sklearn.cross_validation import train_test_split from sklearn.datasets import load_iris # prepare dataset iris = load_iris() X = iris.data[:, :2] y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # svm classification clf = svm.SVC(kernel='rbf', gamma=0.7, C = 1.0).fit(X_train, y_train) y_predicted = clf.predict(X_test) # performance print "Classification report for %s" % clf print print metrics.classification_report(y_test, y_predicted) print print "Confusion matrix" print metrics.confusion_matrix(y_test, y_predicted)

This example would output f1. if you need accuracy, using the code below

metrics.accuracy_score(y_test, y_predicted)

2. Cross Validation

head is the same with the example above, after we have X and y, 

 clf2 = svm.SVC(kernel='linear', gamma=0.7, C = 1.0) # build a svm classifier
 scores = cross_validation.cross_val_score(clf2, X, y, cv = 5) # calculate the accuracy
 scores2 = cross_validation.cross_val_score(clf2, X, y, cv = 5, scoring = 'f1') # calculate the f1
  
 print 'clf2 res:\n'
 print scores.mean()
 print 'clf2 f1:\n'
 print scores2.mean()