Python Logistic 回归优势比 0 或 1

发布于 2025-01-09 20:56:30 字数 3635 浏览 1 评论 0原文

编辑：对于哪个类别，Python sklearn Logistic 回归默认为 0 或 1 提供系数，从而提供优势比。

我想知道我在逻辑回归中得到哪个类别的优势比：

优势比：

params = model.params
conf = model.conf_int()
conf['Odds Ratio'] = params
conf.columns = ['5%', '95%', 'Odds Ratio']
print(np.exp(conf))

所以首先如果 1 = 是并且 0 = 否，那么：

	5%	95%	比值比
常数	2.497035	2.670068	2.582102
x1	1.110917	1.196509	1.152919
x2	1.185360	1.272626	1.228218
x3	2.424528	2.752994	2.583546
x4	1.067706	1.135763	1.101209
x5	1.259765	1.348388	1.303323
x6	7.104122	7.563664	7.330293

反之亦然，0=是， 1=否

	5%	95%	比值
比常量	0.376813	0.402834	0.389606
x1	0.828915	0.892628	0.860182
x2	0.782983	0.840419	0.811193
x3	0.374006	0.424037	0.398237
x4	0.872428	0.927973	0.899772
x5	0.739507	0.791352	0.764990
x6	7.096197	7.554404	7.321717

我认为赔率是 =1，但是根据数据的一些自然假设，至少有一个 Yes = 1 的赔率对我来说没有意义，并且所有低于 Yes = 0 的赔率比（除了上面没有意义的赔率）也没有什么意义。基于我对数据的假设的感觉；但无论如何我想对此事进行一些确认。

原文

Edit:
For which category, 0 or 1 does Python sklearn Logistic Regression by default provides the coefficient and thus the Odds Ratio.

I'm wondering for which category I'm getting my odds ratio in a logistic regression:

Odds ratio:

params = model.params
conf = model.conf_int()
conf['Odds Ratio'] = params
conf.columns = ['5%', '95%', 'Odds Ratio']
print(np.exp(conf))

So first of if 1 = Yes and 0 = No then:

	5%	95%	Odds Ratio
const	2.497035	2.670068	2.582102
x1	1.110917	1.196509	1.152919
x2	1.185360	1.272626	1.228218
x3	2.424528	2.752994	2.583546
x4	1.067706	1.135763	1.101209
x5	1.259765	1.348388	1.303323
x6	7.104122	7.563664	7.330293

And the other way around, 0=yes, 1=no

	5%	95%	Odds Ratio
const	0.376813	0.402834	0.389606
x1	0.828915	0.892628	0.860182
x2	0.782983	0.840419	0.811193
x3	0.374006	0.424037	0.398237
x4	0.872428	0.927973	0.899772
x5	0.739507	0.791352	0.764990
x6	7.096197	7.554404	7.321717

I'm thinking the odds are for =1, however at least one of the odds where Yes = 1 is not making sense to me based on some natural assumptions of the data, and all the odds ratios below where yes = 0, except the one that doesn't make sense above, also make little sense based on my assumptions of the data; but anyway I'd like some confirmation on the matter.

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