Easy_Reference6088 t1_j6ljqlq wrote on January 31, 2023 at 4:23 AM

Reply to comment by ObIivious in ELI5: linear regression and how it plays a role in deep learning by ObIivious

Yep, the line is just an estimation of what a dog of that size would probably eat based on the data with the X axis being weight and Y being the predicted food eaten. It would not be perfect because no dog is the same but it would be a pretty good guess with enough data.

ObIivious OP t1_j6lkdl7 wrote on January 31, 2023 at 4:28 AM

Thank you thank you. I can see how this may apply in deep learning so ill continue to do research. The word regression is what confused me initially.

Easy_Reference6088 t1_j6llp9u wrote on January 31, 2023 at 4:40 AM

Regression is a somewhat cryptic name coined by a guy in the late 1800s. Basically it's referring to the fact if you have enough data, it will eventually head back towards the mean. You can probably google it to learn more because I'm not an authority figure lol.

Spiritual_Jaguar4685 t1_j6n8kvj wrote on January 31, 2023 at 3:15 PM

"Regression" is a math word that means "finding a line that best fits the data".

In your example, "Linear Regression", is just one type of regression that assumes the data wants to fit a straight line.

You hear about it a lot in algorithms because they are all about taking points of data, and trying to figure out what the larger scale process that's controlling the data.

Alternatively you might have something like "exponential regression", where the data wants to fit a curve. For example, if you wanted to chart the spread of COVID-19 in the early days of the pandemic, you'd want to use exponential regression. Linear regression would work at first, but once you started projecting out a week or two you'd see that your line stopped matching the actual infection rate suddenly, and then became very wrong, very quickly.

In practice, linear regression is much, much easier to both do and understand, so it's more "popular". Exponential regression usually requires computer analysis and is more confusing to lay-people. That's just why you hear about "linear regression" more commonly.