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=200)

Arguments

n number of patterns to create
sd Standard deviation of noise. The default value of 200 gives a signal to noise ratio of 3:1.

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.


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