| mlbench.friedman2 {mlbench} | R Documentation |
The regression problem Friedman 2 as described in Friedman (1991) and Breiman (1996). Inputs are 4 independent variables uniformly distrtibuted over the ranges
0 <= x1 <= 100
40 π <= x2 <= 560 π
0 <= x3 <= 1
1 <= x4 <= 11
The outputs are created according to the formula
y = (x1^2 + (x2 x3 - (1/(x2 x4)))^2)^{0.5} + e
where e is N(0,sd).
mlbench.friedman2(n, sd=125)
n |
number of patterns to create |
sd |
Standard deviation of noise. The default value of 125 gives a signal to noise ratio (i.e., the ratio of the standard deviations) of 3:1. Thus, the variance of the function itself (without noise) accounts for 90% of the total variance. |
Returns a list with components
x |
input values (independent variables) |
y |
output values (dependent variable) |
Breiman, Leo (1996) Bagging predictors. Machine Learning 24, pages 123-140.
Friedman, Jerome H. (1991) Multivariate adaptive regression splines. The Annals of Statistics 19 (1), pages 1-67.