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早上在使用NuSVC进行模型训练的时候，报错如下

Reloaded modules: __mp_main__Traceback (most recent call last):  File "
   
    ", line 1, in 
    
         runfile  File "C:\Users\peter\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile    execfile(filename, namespace)  File "C:\Users\peter\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile    exec(compile(f.read(), filename, 'exec'), namespace)  File "C:/Users/TfidfVectorizer-svm.py", line 58, in 
     
          clf = svm.NuSVC(nu=0.1).fit(X_train, y_train)  File "C:\Users\peter\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\svm\base.py", line 187, in fit    fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)  File "C:\Users\peter\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\svm\base.py", line 276, in _sparse_fit    random_seed)  File "sklearn/svm/libsvm_sparse.pyx", line 144, in sklearn.svm.libsvm_sparse.libsvm_sparse_trainValueError: b'specified nu is infeasible'
     
    
   

我查阅资料后发现是因为NuSVC参数nu的设置问题，使用如下代码一个个尝试nu参数的合适的值。

nus =[_/10 for _ in range(1,11,1)]for nu in nus:     clf = svm.NuSVC(nu=nu)     try:         clf.fit(X_train, y_train)#替换成自己的训练模型     except ValueError as e:         print("nu {} not feasible".format(nu))

当然另外一种解决方案就是把NuSVC换成LinearSVC，我发现LinearSVC训练的效果更好。

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