Timeline for Machine learning with some constraints
Current License: CC BY-SA 4.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 14, 2019 at 10:29 | vote | accept | Yoo Inhyeok | ||
| Nov 11, 2019 at 9:18 | comment | added | quester | @YooInhyeok no it's more choice of "decision function"/"head for NN" to use sigmoid-like because this approach guarantees that model will output values from certain interval and use l2 loss because you would like to model how much units you would like to order so it's a regression problem | |
| Nov 11, 2019 at 8:50 | comment | added | Yoo Inhyeok | @quester So changing a regression problem to a classification problem is the answer? | |
| Nov 9, 2019 at 3:01 | history | tweeted | twitter.com/StackStats/status/1193000510191747072 | ||
| Nov 8, 2019 at 21:46 | comment | added | quester | if demand $\in [0, 50]$ then you can normalize it to $[0,1]$ and use logistic regression and use returned $probs$ (or any sigmoid-like head for NN)..., also machine learning require data to learn (upfront or by reinforced learning), because you have to find correct values of coefficients in your model | |
| Nov 8, 2019 at 16:42 | answer | added | Tim | timeline score: 1 | |
| Nov 8, 2019 at 9:43 | history | edited | Yoo Inhyeok | CC BY-SA 4.0 |
deleted 4 characters in body
|
| Nov 8, 2019 at 9:35 | comment | added | Yoo Inhyeok | @carlo For examples, demand must be positive numbers because it can never be negative. However, a linear regression line can result in negative demand. So I want to add this positive condition on my machine. | |
| Nov 8, 2019 at 9:31 | comment | added | Yoo Inhyeok | @carlo There are no data. I asked it just curious. Can you explain how can I make my own loss function to me more details? | |
| Nov 8, 2019 at 9:28 | comment | added | carlo | I'd sure go for a personalized loss function. however, there is more to say if we knew better your problem and your data. | |
| Nov 8, 2019 at 9:13 | history | asked | Yoo Inhyeok | CC BY-SA 4.0 |