Timeline for How to add bias consideration into logistic regression code?
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 1, 2021 at 5:32 | comment | added | David Dale | @Ambleu pleas never mind about this "sometimes", I don't know why I wrote it. | |
| Feb 1, 2021 at 1:49 | comment | added | haneulkim | Absolutely amazing! the way you approach curious question is very motivating. Thanks you for your knowledge and motivation! One thing I want to make sure, why in your gist does it say "inverse logistic function is sometimes called logit" why sometimes? is there a time it isn't called a logit function? After doing some research I couldn't find when it is not called a logit function. | |
| Jan 31, 2021 at 20:10 | comment | added | David Dale | @Ambleu I got curious and created a notebook which demonstrates that initialization of logistic regression with coefficients from linear regression can speed up training. gist.github.com/avidale/a640f7a8e353d9efdd79385e277caef1 | |
| Jan 31, 2021 at 16:55 | comment | added | David Dale | @Ambleu if you are talking about starting values for gradient descent, then exact zeros or small random numbers around zero would make do. As an alternative, you may try to initialize the logistic regression from the linear regression line by making them tangent at the center of your data. May be, it will save you a few gradient descent steps. But in general, starting values don't matter much for logistic regression, because its loss function is convex and if there is an optimum, gradient descent is guaranteed to converge to it from anywhere. | |
| Jan 31, 2021 at 13:03 | comment | added | haneulkim | What is default value for β0 (bias term) and also for other weights of features (β1, β2, etc...)? | |
| Nov 15, 2017 at 13:33 | vote | accept | DN1 | ||
| Nov 15, 2017 at 12:57 | history | answered | David Dale | CC BY-SA 3.0 |