我一直在使用sklearn的模型（SVM，Logistic回归，MLP，......）直到昨天我没有任何问题，但我不知道为什么，目前当我尝试适合模型时，这需要夸大其词多少时间 .
例如，对于具有6个特征的551个样本，尝试使用多项式内核拟合支持向量机并改变参数：
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C = 1.00度= 1.00 Coef = 0.000 Gamma = 0.25 15.124秒 .
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C = 1.00度= 1.00 Coef = 0.000 Gamma = 0.75 22.937秒 .
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C = 1.00度= 1.00 Coef = 3.000 Gamma = 0.25 11.703秒
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C = 1.00度= 1.00 Coef = 3.000 Gamma = 0.75 18.81秒
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C = 1.00度= 2.00 Coef = 0.000 Gamma = 0.25 316.115秒
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C = 1.00度= 2.00 Coef = 0.000 Gamma = 0.75 74.530秒
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C = 1.00度= 2.00 Coef = 3.000 Gamma = 0.25 270.514秒
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C = 1.00度= 2.00 Coef = 3.000 Gamma = 0.75 357.194秒
from sklearn import svm
for C in C_values:
for degree in degree_values:
for coef in coef_values:
for gamma in gamma_values:
print('C = {0:.2f} Degree = {1:.2f} Coef = {2:.3f} Gamma = {3:.2f} '.format(C,degree,coef,gamma))
clf = svm.SVC(C = C, degree = degree, gamma = gamma, coef0 = coef, kernel = 'poly', decision_function_shape = decision_function_shape )
time_i = time()
clf.fit(X_train, Y_train)
time_f = time()
print('str(time_f - time_i) '+'seconds')
我从昨天到今天没有任何改变，我认为5分钟适合550个样本和6个功能的模型被夸大了 .
什么可以产生这种增加的时间？
谢谢
EDIT ：
我一直在尝试使用random_state参数不同的时间，结果是类似的：
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C = 1.00度= 1.00 Coef = 0.000 Gamma = 0.25 ...... 7.359 scnds
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C = 1.00度= 1.00 Coef = 0.000 Gamma = 0.75 ...... 26.156 scnds
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C = 1.00度= 1.00 Coef = 3.000 Gamma = 0.25 ...... 8.781 scnds
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C = 1.00度= 1.00 Coef = 3.000 Gamma = 0.75 ...... 16.437 scnds
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C = 1.00度= 2.00 Coef = 0.000 Gamma = 0.25 ..... 259.248 scnds
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C = 1.00度= 2.00 Coef = 0.000 Gamma = 0.75 ...... 219.170 scnds
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C = 1.00度= 2.00 Coef = 3.000 Gamma = 0.25 ...... 302.201 scnds
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C = 1.00度= 2.00 Coef = 3.000 Gamma = 0.75 ...... 345.163 scnds