Timeline for True Error of a binary classifier
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 30, 2019 at 19:25 | vote | accept | Shiv Tavker | ||
| Jun 30, 2019 at 11:22 | vote | accept | Shiv Tavker | ||
| Jun 30, 2019 at 18:10 | |||||
| Jun 30, 2019 at 10:20 | answer | added | Yuval Filmus | timeline score: 0 | |
| Jun 30, 2019 at 8:24 | comment | added | Shiv Tavker | Expanding the first one, we get Pr[h(x)=1, y=0] + Pr[h(x)=0, y=1]. If we assume h(x) = 0, that is Pr[h(x)=0] = 1. The term boils down to Pr[y≠0/h(x)=0] which is different from Pr[y≠0/x] right? | |
| Jun 30, 2019 at 8:16 | comment | added | Yuval Filmus | Yes, the two formulas are equivalent. Now try to see why this is the case. The first step is to understand the notations. | |
| Jun 29, 2019 at 19:55 | review | First posts | |||
| Jun 30, 2019 at 2:00 | |||||
| Jun 29, 2019 at 19:51 | history | asked | Shiv Tavker | CC BY-SA 4.0 |