mlbench.friedman3 {mlbench} | R Documentation |
The regression problem Friedman 3 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 = atan ((x2 x3 - (1/(x2 x4)))/x1) + e
where e is N(0,sd).
mlbench.friedman3(n, sd=0.2)
n |
number of patterns to create |
sd |
Standard deviation of noise. The default value of 0.2 gives a signal to noise ratio of 3:1. |
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.