如何在python中使用libSVM计算精度,召回率和F-score

Question

我想计算precision,recall并在Python中f-score使用libsvm,但我不知道如何.我找到了这个网站,但我不知道如何调用该函数,如果你可以帮助我通过例子.

Answer 1

您可以利用scikit-learn,这是Python中机器学习的最佳软件包之一.它的SVM实现使用libsvm,你可以计算精度,召回和f-score,如下面的代码片段所示:

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)

这将产生类似于此的输出:

Classification report for SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.7,
kernel=rbf, max_iter=-1, probability=False, shrinking=True, tol=0.001,
verbose=False)

             precision    recall  f1-score   support

          0       1.00      1.00      1.00         9
          1       0.90      0.69      0.78        13
          2       0.64      0.88      0.74         8

avg / total       0.86      0.83      0.84        30


Confusion matrix
[[9 0 0]
 [0 9 4]
 [0 1 7]]

当然,您可以使用libsvm tools您提到的,但它们只能用于二进制分类,而scikit您可以使用多类.