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对于相同的数据集和参数，我对LibSVM和scikit-learn的 SVM 实现获得不同的准确性，即使在内部scikit-learn也使用LibSVM.

我忽略了什么？

LibSVM 命令行版本：

me@my-compyter:~/Libraries/libsvm-3.16$ ./svm-train -c 1 -g 0.07 heart_scale heart_scale.model
optimization finished, #iter = 134
nu = 0.433785
obj = -101.855060, rho = 0.426412
nSV = 130, nBSV = 107
Total nSV = 130
me@my-compyter:~/Libraries/libsvm-3.16$ ./svm-predict heart_scale heart_scale.model heart_scale.result
Accuracy = 86.6667% (234/270) (classification)

Scikit-learn NuSVC 版本：

In [1]: from sklearn.datasets import load_svmlight_file    
In [2]: X_train, y_train = load_svmlight_file('heart_scale')    
In [3]: from sklearn import svm    
In [4]: clf = svm.NuSVC(gamma=0.07,verbose=True)   
In [5]: clf.fit(X_train,y_train)
        [LibSVM]*
        optimization finished, #iter = 118
        C = 0.479830
        obj = 9.722436, rho = -0.224096
        nSV = 145, nBSV = 125
        Total nSV = 145
Out[5]: NuSVC(cache_size=200, coef0=0.0, degree=3, gamma=0.07, kernel='rbf',
        max_iter=-1, nu=0.5, probability=False, shrinking=True, tol=0.001,
        verbose=True)
In [6]: pred = clf.predict(X_train)    
In [7]: from sklearn.metrics import accuracy_score    
In [8]: accuracy_score(y_train, pred)
Out[8]: 0.8481481481481481

Scikit-learn SVC 版本：

In [1]: from sklearn.datasets import load_svmlight_file    
In [2]: X_train, y_train = load_svmlight_file('heart_scale')    
In [3]: from sklearn import svm    
In [4]: clf = svm.SVC(gamma=0.07,C=1, verbose=True)   
In [5]: clf.fit(X_train,y_train)
        [LibSVM]*
        optimization finished, #iter = 153
        obj = -101.855059, rho = -0.426465
        nSV = 130, nBSV = 107
        Total nSV = 130
Out[5]: SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.07,
        kernel='rbf', max_iter=-1, probability=False, shrinking=True, tol=0.001,
        verbose=True)
In [6]: pred = clf.predict(X_train)    
In [7]: from sklearn.metrics import accuracy_score    
In [8]: accuracy_score(y_train, pred)
Out[8]: 0.8666666666666667

更新

Update1：​​将 scikit-learn 示例从 SVR 更新为 NuSVC，请参阅 ogrisel 的回答

更新2：添加了输出verbose=True

Update3：添加了 scikit-learn SVC 版本

所以看起来我的问题已经解决了。如果我使用 SVCC=1而不是 NuSVC，我会得到与 libsvm 相​​同的结果，但是有人可以解释为什么 NuSVC 和 SVC(C=1) 会给出不同的结果，即使它们应该做同样的事情（参见 ogrisel 的回答）？

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1 回答 1

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SVR是回归模型，不是分类模型。svm-train -c 1是作为sklearn.svm.NuSVC类可用的 Nu-SVC 模型。

于 2013-03-06T18:11:40.103 回答