Benchmark Problem Friedman 1

Description

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

Value

Returns a list with components

References

Breiman L (1996). “Bagging Predictors.” Machine Learning, 24(2), 123–140. doi:10.1023/a:1018054314350. Friedman JH (1991). “Multivariate Adaptive Regression Splines.” The Annals of Statistics, 19(1), 1–67. doi:10.1214/aos/1176347963.