So, where do the values of 1.5 or -33.2—which were causing you trouble with the regression numbers the professor used as an example—actually apply?
Which course? (Course name, video title, and time mark please).
You’ve posted in the “AI Discussions” forum. That isn’t specific to any individual course.
Hello Tmosh
Sorry:
https://www.coursera.org/learn/machine-learning/lecture/1ACA2/linear-regression-model-part-1
Linear regression model part 1
03:30"
I’ve moved this thread to the correct forum area for that course.
thanks
Hello @John_Ramos,
They were not applied anywhere. They were not numbers inside a model. They were just two examples out of infinitely many possible values that a regression model may produce as prediction results. They contrast with a binary classification model which may only give us either 0 or 1.
Below is some transcript of what Andrew talked about next in the same video.
As a reminder about the difference between classification and regression, in classification, there are only a small number of possible outputs. If your model is recognizing cats versus dogs, that’s two possible outputs. Or maybe you’re trying to recognize any of 10 possible medical conditions in a patient, so there’s a discrete, finite set of possible outputs.
We call it classification problem, whereas in regression, there are infinitely many possible numbers that the model could output.
Cheers,
Raymond