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我正在尝试通过使用交叉验证的网格参数搜索来优化 scikit-learn 中的逻辑回归函数，但我似乎无法实现它。

它说逻辑回归没有实现 get_params() 但在文档上它说它确实。我该如何根据我的基本事实优化此功能？

>>> param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000] }
>>> clf = GridSearchCV(LogisticRegression(penalty='l2'), param_grid)
>>> clf
GridSearchCV(cv=None,
       estimator=LogisticRegression(C=1.0, intercept_scaling=1, dual=False, fit_intercept=True,
          penalty='l2', tol=0.0001),
       fit_params={}, iid=True, loss_func=None, n_jobs=1,
       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},
       pre_dispatch='2*n_jobs', refit=True, score_func=None, verbose=0)
>>> clf = clf.fit(gt_features, labels)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/scikit_learn-0.14_git-py2.7-macosx-10.8-x86_64.egg/sklearn/grid_search.py", line 351, in fit
    base_clf = clone(self.estimator)
  File "/Library/Python/2.7/site-packages/scikit_learn-0.14_git-py2.7-macosx-10.8-x86_64.egg/sklearn/base.py", line 42, in clone
    % (repr(estimator), type(estimator)))
TypeError: Cannot clone object 'LogisticRegression(C=1.0, intercept_scaling=1, dual=False, fit_intercept=True,
          penalty='l2', tol=0.0001)' (type <class 'scikits.learn.linear_model.logistic.LogisticRegression'>): it does not seem to be a scikit-learn estimator a it does not implement a 'get_params' methods.
>>>
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2 回答 2

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类名scikits.learn.linear_model.logistic.LogisticRegression指的是一个非常古老的 scikit-learn 版本。顶级包名称现在sklearn至少有 2 或 3 个版本。您很可能在您的 python 路径中同时安装了旧版本的 scikit-learn。将它们全部卸载，然后重新安装 0.14 或更高版本，然后重试。

于 2013-09-26T09:29:31.077 回答
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您还可以将惩罚作为参数与 CEg 一起提供：

grid_values = {'penalty': ['l1','l2'], 'C': [0.001,0.01,0.1,1,10,100,1000]}. 接着，model_lr = GridSearchCV(lr, param_grid=grid_values)

于 2017-08-24T07:57:17.943 回答