Timeline for When should linear regression be called "machine learning"?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 22, 2017 at 23:20 | comment | added | Patrick B. | I found Akavall's reply pretty clear. I believe Akavall's problem is that the definition you present is circular, because it appears to boil down to "Q: when does technique X count as 'machine learning'? A: when technique X is performed using a definable element of machine learning." (Unfortunately I don't understand the second point you're making so I can't respond to that.) | |
| Mar 21, 2017 at 5:30 | comment | added | Carl | I do not understand what you wrote, grammatically. I am also trying to understand also the linguistic torture of taking a process, i.e., learning, and treating it as if it were not a process. What I see so far is nonsense. | |
| Mar 21, 2017 at 3:59 | comment | added | Akavall | @Carl, the problem here that "machine learning" defined. To me if we can use a statistical model, and that model would have ability to predict it is machine learning. And it does not matter what approach was used to find the coefficients of the model. | |
| Mar 21, 2017 at 2:06 | comment | added | Carl | So the answer to the OP question When is linear regression machine learning, as opposed to simply finding a best-fit line? When linear regression is performed using a definable element of machine learning, like gradient descent, it is then linear regression performed using machine learning. | |
| Mar 21, 2017 at 2:06 | comment | added | Carl | I am not the only person to use this exact same example, see here. On gradient descent for machine learning. | |
| Mar 21, 2017 at 2:05 | comment | added | Carl | Thus conceptually, linear regression via gradient descent (learning) chooses better and better summed square residuals (loss function). The basic concepts are the same as those for much more advanced learning algorithms, such as neural networks. These algorithms simply replace the linear model with a much more complex model - and, correspondingly, a much more complex cost function.. | |
| Mar 21, 2017 at 2:04 | comment | added | Carl | There is actually a difference, although linear regression can be solved using machine learning. A common regression target is ordinary least squares, which means, that our target loss function, sum squared residuals, is to be minimized. Now, machine learning would simply refer to that method by which we minimize a loss function. | |
| Mar 21, 2017 at 1:07 | history | answered | Akavall | CC BY-SA 3.0 |