How to reduce the rmse for a fit obtained using curve fitting toolbox

7 visualizaciones (últimos 30 días)
Hi I need to find a function which best fits x and y data points as given below
xdata=[0.500000000000000,0.700000000000000,1];
ydata=[7862.13480751439,5801.18185697929,4993.13280898239];
When I use Curve fitting toolbox, it returns a polynomial function with the goodness of fit as given below
Linear model Poly2:
f(x) = p1*x^2 + p2*x + p3
Coefficients:
p1 = 1.522e+04
p2 = -2.857e+04
p3 = 1.834e+04
Goodness of fit:
SSE: 1.414e-22
R-square: 1
Adjusted R-square: NaN
RMSE: NaN
ydata points appears to coincide the curve in the plot and also the sse and Rsquare are desirable but the rmse returned by the goodness of fit is NaN. Can this rmse be negelected? How is Adjusted R-square value calculated?

Respuesta aceptada

Ahmet Cecen
Ahmet Cecen el 10 de Nov. de 2016
Read up on the concepts of Overfitting, Underfitting, Variance and Regression. You are fitting a function of 3 variables to 3 data points. I would say a regression problem with 3 data points is fairly meaningless to begin with, but if you have to do it, fit a line instead.

Más respuestas (0)

Categorías

Más información sobre Linear and Nonlinear Regression en Help Center y File Exchange.

Etiquetas

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by