Ratio de cotes de régression logistique des modèles de stats
>>> import statsmodels.api as sm
>>> import numpy as np
>>> X = np.random.normal(0, 1, (100, 3))
>>> y = np.random.choice([0, 1], 100)
>>> res = sm.Logit(y, X).fit()
Optimization terminated successfully.
Current function value: 0.683158
Iterations 4
>>> res.summary()
<class 'statsmodels.iolib.summary.Summary'>
"""
Logit Regression Results
==============================================================================
Dep. Variable: y No. Observations: 100
Model: Logit Df Residuals: 97
Method: MLE Df Model: 2
Date: Sun, 05 Jun 2016 Pseudo R-squ.: 0.009835
Time: 23:25:06 Log-Likelihood: -68.316
converged: True LL-Null: -68.994
LLR p-value: 0.5073
==============================================================================
coef std err z P>|z| [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1 -0.0033 0.181 -0.018 0.985 -0.359 0.352
x2 0.0565 0.213 0.265 0.791 -0.362 0.475
x3 0.2985 0.216 1.380 0.168 -0.125 0.723
==============================================================================
"""
>>>
Clumsy Crocodile