我在进行建模时使用了Logistic Regression。但是在尝试不同的solver时，当我设置solver = “multinomial”时，出现了以下错误：

import sklearn as sklskl.__version__'0.21.2'X_train, X_test, y_train, y_test = train_test_split(multiclass_logistic_data, labels, test_size = 0.2, random_state = 1)cv_reg = linear_model.LogisticRegressionCV(solver='multinomial', max_iter=1000)cv_reg.fit(X_train, y_train)

ValueError                                Traceback (most recent call last)<ipython-input-54-6d16d00d0653> in <module>----> 1 cv_reg.fit(X_train, y_train)E:\Anaconda_Install\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight)   1970         self : object   1971         """-> 1972         solver = _check_solver(self.solver, self.penalty, self.dual)   1973    1974         if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0:E:\Anaconda_Install\lib\site-packages\sklearn\linear_model\logistic.py in _check_solver(solver, penalty, dual)    435     if solver not in all_solvers:    436         raise ValueError("Logistic Regression supports only solvers in %s, got"--> 437                          " %s." % (all_solvers, solver))    438     439     all_penalties = ['l1', 'l2', 'elasticnet', 'none']ValueError: Logistic Regression supports only solvers in ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga'], got multinomial.

回答：

请将solver='multinomial'替换为multi_class='multinomial'。没有名为’multinomial’的solver。

在评论中你提到，

我在educative inc的课程中阅读了关于solver的参考资料

不要在其他地方阅读参考资料或文档，请使用scikit-learn的网站，https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html

请阅读文档。