我正在尝试通过更改其参数来调整我的逻辑回归模型。
我的代码:
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_
但是这个错误出来了:
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:ProgramDataAnaconda3libsite-packagessklearngrid_search.py in
fit(self, X, y)
827
828 """
--> 829 return self._fit(X, y, ParameterGrid(self.param_grid))
830
831
C:ProgramDataAnaconda3libsite-packagessklearngrid_search.py in
_fit(self, X, y, parameter_iterable)
559 n_candidates * len(cv)))
560
--> 561 base_estimator = 克隆(自我估计器( 562 563 pre_dispatch = self.pre_dispatch
C:ProgramDataAnaconda3libsite-packagessklearnbase.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'
这里有关于我做错了什么的任何建议?
您需要将估计器初始化为实例,而不是将类直接传递给GridSearchCV:
lr = LogisticRegression() # initialize the model
grid = GridSearchCV(lr, param_grid, cv=12, scoring = 'accuracy', )
grid.fit(X5, y5)