Skip to main content

Linear Regression

· 3 min read
Panos Karagiannis
NLP Engineer

In the context of linear regression we are trying to solve a very simple problem. Given a sequence of pairs of points ((x1,y1)(xn,yn))((x_1, y_1) \dots (x_n, y_n)) we are trying to find the polynomial that best fits these points with respect to a loss function (least squares is very commonly used in these cases). Essentially we are trying to come up with a function of the form:

y=β0x0+β1x+β2x2+βkxky = \beta_0 x^0 + \beta_1 x + \beta_2 x^2 + \dots \beta_k x^k