Python scikit-learn 中 LogisticRegression 上的 GridSearchCV
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/19018333/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
GridSearchCV on LogisticRegression in scikit-learn
提问by genekogan
I am trying to optimize a logistic regression function in scikit-learn by using a cross-validated grid parameter search, but I can't seem to implement it.
我试图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。
It says that Logistic Regression does not implement a get_params() but on the documentation it says it does. How can I go about optimizing this function on my ground truth?
它说逻辑回归没有实现 get_params() 但在文档中它说它实现了。我怎样才能根据我的真实情况优化这个功能？
>>> 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.
>>>
采纳答案by ogrisel
The class name scikits.learn.linear_model.logistic.LogisticRegressionrefers to a very old version of scikit-learn. The top level package name is now sklearnsince at least 2 or 3 releases. It's very likely that you have old versions of scikit-learn installed concurrently in your python path. Uninstall them all, then reinstall 0.14 or later and try again.
类名scikits.learn.linear_model.logistic.LogisticRegression指的是一个非常旧的 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 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 或更高版本并重试。
回答by Biswajit Ghoshal
You can also give penalty as a parameter along with C. E.g. :
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]}. and then, model_lr = GridSearchCV(lr, param_grid=grid_values)
grid_values = {'penalty': ['l1','l2'], 'C': [0.001,0.01,0.1,1,10,100,1000]}. 进而，model_lr = GridSearchCV(lr, param_grid=grid_values)
