The regression problem Friedman 1 as described in Friedman (1991) and
Breiman (1996). Inputs are 10 independent variables uniformly
distributed on the interval $[0,1]$, only 5 out of these 10 are actually
used. Outputs are created according to
the formula
$$y = 10 \sin(\pi x1 x2) + 20 (x3 - 0.5)^2 + 10 x4 + 5 x5 + e$$
where e is N(0,sd).
Usage
mlbench.friedman1(n, sd=1)
Arguments
n
number of patterns to create
sd
Standard deviation of noise
Value
Returns a list with components
xinput values (independent variables)
youtput values (dependent variable)
References
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.