我有一个多类分类逻辑回归模型。使用一个非常基本的 sklearn 管道，我正在接受对象的净化文本描述并将所述对象分类到一个类别中。
logreg = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', LogisticRegression(n_jobs=1, C=cVal)),
])
最初，我从 C = 1e5 的正则化强度开始，在我的测试集上实现了 78% 的准确率，在我的训练集上实现了近 100% 的准确率（不确定这是否常见）。然而，即使模型达到了合理的精度，我也被警告说模型没有收敛，我应该增加最大迭代次数或缩放数据。
ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
更改 max_iter 没有任何作用，但是修改 C 可以使模型收敛，但会导致精度下降。以下是测试不同 C 值的结果：
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C = 0.1
Model trained with accuracy 0.266403785488959 in 0.99mins
maxCoeff 7.64751682657047
aveProb 0.1409874146376454
[0.118305 0.08591412 0.09528015 ... 0.19066049 0.09083797 0.0999868 ]
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C = 1
Model trained with accuracy 0.6291798107255521 in 1.72mins
maxCoeff 16.413911220284994
aveProb 0.4221365866656076
[0.46077294 0.80758323 0.12618175 ... 0.91545935 0.79839096 0.13214606]
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(no converge)
C = 10
Model trained with accuracy 0.7720820189274448 in 1.9mins
maxCoeff 22.719712528228182
aveProb 0.7013386216302577
[0.92306384 0.97842762 0.71936027 ... 0.98604736 0.98845931 0.20129053]
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(no converge)
C = 100
Model trained with accuracy 0.7847003154574133 in 1.89mins
maxCoeff 40.572468674674916
aveProb 0.8278969567537955
[0.98949986 0.99777337 0.94394682 ... 0.99882797 0.99992239 0.28833321]
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(no converge)
C = 1000
Model trained with accuracy 0.7796529968454259 in 1.85mins
maxCoeff 72.19441171771533
aveProb 0.8845385182334065
[0.99817968 0.99980068 0.98481744 ... 0.9999964 0.99999998 0.36462353]
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(no converge)
C = 10000
Model trained with accuracy 0.7757097791798108 in 1.88mins
maxCoeff 121.56900229473293
aveProb 0.9351308553465546
[0.99994777 0.99999677 0.98521023 ... 0.99999987 1. 0.48251051]
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(no converge)
C = 100000
Model trained with accuracy 0.7785488958990536 in 1.84mins
maxCoeff 160.02719692775156
aveProb 0.9520556562102963
[0.99999773 0.99999977 0.98558839 ... 0.99999983 1. 0.54044361]
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如您所见，模型训练仅在 1e-3 到 1 之间的 C 值处收敛，但未达到较高 C 值不收敛时所见的准确度。
更新：这里是 C = 1 和 C = 1e5 的学习曲线。正如我之前提到的，训练曲线似乎总是 1 或接近 1 (0.9999999)，C 值很高并且没有收敛，但是在优化收敛的 C = 1 的情况下，情况看起来要正常得多。这对我来说似乎很奇怪......
C = 1，收敛
C = 1e5，不收敛
这是测试不同求解器的结果
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Solver = newton-cg
Model trained with accuracy 0.7810725552050474 in 6.23mins
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ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)
Solver = lbfgs
Model trained with accuracy 0.7847003154574133 in 1.93mins
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Solver = liblinear
Model trained with accuracy 0.7779179810725552 in 0.27mins
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ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
Solver = sag
Model trained with accuracy 0.7818611987381704 in 0.47mins
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ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
"the coef_ did not converge", ConvergenceWarning)
Solver = saga
Model trained with accuracy 0.782018927444795 in 0.54mins
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这是常见的行为吗？基于这种行为，任何人都可以判断我是否以错误的方式进行此操作？
