sim_Friedman1 {bark} R Documentation

## Simulated Regression 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^2).

### Usage

sim_Friedman1(n, sd = 1)


### Arguments

 n number of data points to create sd standard deviation of noise, with default value 1

### 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.

Other bark simulation functions: sim_Friedman2(), sim_Friedman3(), sim_circle()
Other bark functions: bark-package-deprecated, bark-package, bark(), sim_Friedman2(), sim_Friedman3(), sim_circle()
sim_Friedman1(100, sd=1)