Python Logistic Regression Odds Ratio 0 or 1-CodePudding

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.

CodePudding user response：

I think I finally found my answer:

"Because of the logit function, logistic regression coefficients represent the log odds that an observation is in the target class (“1”) given the values of its X variables."