我正在尝试通过更改参数来调整我的Logistic Regression模型。

我的代码如下：

solver_options = ['newton-cg', 'lbfgs', 'liblinear', 'sag']multi_class_options = ['ovr', 'multinomial']class_weight_options = ['None', 'balanced']param_grid = dict(solver = solver_options, multi_class = multi_class_options, class_weight = class_weight_options)grid = GridSearchCV(LogisticRegression, param_grid, cv=12, scoring = 'accuracy')grid.fit(X5, y5)grid.grid_scores_

但这会导致错误：

TypeError                                 Traceback (most recent call last)<ipython-input-84-6d812a155800> in <module>()    1 param_grid = dict(solver = solver_options, multi_class = multi_class_options, class_weight = class_weight_options)    2 grid = GridSearchCV(LogisticRegression, param_grid, cv=12, scoring = 'accuracy')----> 3 grid.fit(X5, y5)      4 grid.grid_scores_C:\ProgramData\Anaconda3\lib\site-packages\sklearn\grid_search.py in fit(self, X, y)    827     828         """--> 829         return self._fit(X, y, ParameterGrid(self.param_grid))    830     831 C:\ProgramData\Anaconda3\lib\site-packages\sklearn\grid_search.py in _fit(self, X, y, parameter_iterable)559                                          n_candidates * len(cv)))560 

–> 561 base_estimator = clone(self.estimator) 562 563 pre_dispatch = self.pre_dispatch

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\base.py in clone(estimator, safe)     65                             % (repr(estimator), type(estimator)))     66     klass = estimator.__class__---> 67     new_object_params = estimator.get_params(deep=False)     68     for name, param in six.iteritems(new_object_params):     69         new_object_params[name] = clone(param, safe=False)TypeError: get_params() missing 1 required positional argument: 'self'

这里有什么建议可以告诉我我做错了什么吗？

回答：

你需要将estimator初始化为一个实例，而不是直接将类传递给GridSearchCV：

lr = LogisticRegression()             # 初始化模型grid = GridSearchCV(lr, param_grid, cv=12, scoring = 'accuracy', )grid.fit(X5, y5)