mlbench.friedman2 {mlbench} | R Documentation |

## Benchmark Problem Friedman 2

### Description

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).

### Usage

mlbench.friedman2(n, sd=125)

### Arguments

`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. |

### Value

Returns a list with components

`x` |
input values (independent variables) |

`y` |
output 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.

[Package

*mlbench* version 1.0-0

Index]