Timeline for sklearn: SGDClassifier yields lower accuracy than LogisticRegression
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| Dec 25, 2019 at 15:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Oct 15, 2019 at 10:44 | comment | added | Uday | LogisticRegression uses LIBLINEAR optimization if you are not mentioning any optimization method(solver) as an argument which is different than basic SGD which is used in SGDClassifier so you may get good results on one of them, that totally depends on how we converged using that optimization technique and data we have. | |
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| Dec 1, 2017 at 19:08 | answer | added | PrestonH | timeline score: 2 | |
| Dec 1, 2017 at 18:57 | comment | added | PrestonH |
Are you sure? From the documentation it states that C is the inverse of the regularization strength and alpha is 'Constant that multiplies the regularization term'. I think that implies alpha = 1/C, which is consistent with other notation I've seen elsewhere. In my case I set C=alpha=1 so that the regularization penalty should be the same.
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| Dec 1, 2017 at 8:10 | comment | added | Michael M | Try playing around with different values of alpha. Its meaning is different from C. It might have quite an impact because the number of features is too high compared to the sample size. | |
| Nov 30, 2017 at 17:02 | history | edited | PrestonH | CC BY-SA 3.0 |
Added some code that was missing.
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| Nov 30, 2017 at 17:00 | comment | added | PrestonH | Yes, but I originally missed it when I copied the code into this post. The original post has been updated. | |
| Nov 30, 2017 at 10:58 | comment | added | El Burro | Is the input scaled? | |
| Nov 30, 2017 at 6:51 | history | edited | PrestonH | CC BY-SA 3.0 |
added 1 character in body
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| Nov 30, 2017 at 6:08 | review | First posts | |||
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| Nov 30, 2017 at 6:05 | history | asked | PrestonH | CC BY-SA 3.0 |