- **Homework 2**
(*http://book.caltech.edu/bookforum/forumdisplay.php?f=131*)

- - **Insight re Linear Regression (HW#2 Q5)**
(*http://book.caltech.edu/bookforum/showthread.php?t=9561*)

Insight re Linear Regression (HW#2 Q5)I've been out of school for a long time and so basic math is a bit slow to process. When it comes to the particular linear regression described in the lecture and employed in Q5, I had an insight to share:
This is not linear regression in 2D. It is linear regression in 3D. You are taking 100 points in the xy plane, to each of which is assigned a value z_i (determined by the target function that you defined by taking two NEW random points and generating a line through them). z_i is either +1 or -1. Then, by employing the pseudo-inverse process outlined in the lecture, you are finding a 2D PLANE (w0 + w1*x1 + w2 *x2 = z) in the 3D xyz space by linear regression. The intersection of this regression plane with the xy plane defines your hypothesis g, which mostly separates the N points by their z_i values. You get the equation of this line g by setting the value of z to zero (w0 + w1*x1 + w2 *x2 = 0). |

All times are GMT -7. The time now is 08:20 AM. |

Powered by vBulletin® Version 3.8.3

Copyright ©2000 - 2022, Jelsoft Enterprises Ltd.

The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.