In Singaporean Maths, is covered in 7th Grade.
I learnt it as y = mx + c
gradient
I don't know if is correct. It should be predicting a linear map from two variables to one, since we predict a third value based of any two values. The issue is that the line would only make predictions for a subset of the two values used to predict. Not trying to be pedantic, just not sure if I am understanding correctly
One question: In you noticed that you made a slight mistake, and as a result, you swapped y_train with x_test. How did you understand that it's a case to swapped those two? In other words, what was your logic to figure it out from the error message? BTW. doing tutorials with mistakes is cool, I see it more beneficial than having a clean tutorial without mistakes and without explaining those mistakes.
Does anyone know why at Tim swaps the x_test and y_train variables??? Or more specifically why his original code produces the error?
small question: if I try to run the program from , it keeps giving the error:
I'm using a different file from UCI and at I'm getting this error: TypeError: fit() missing 1 required positional argument: 'y'. Anyone knows what this might indicate? Maybe my dataset doesn't have labels for Y attributes?
I got 0.81 XD
is the 'acc' variable supposed to output different values? that's what's happening for me
at where the coeffiecients are prented, why are there 5 of them? what are those 5 variables?
At , a line in 6 dimentional space would need 5 coef, just like in 2 dimentional space only 1 coef is needed.
: I think it's a 6-dimensional space as a 2-dimensional space function depends of 1 coefficient, not 2.
