mlbench.friedman3 {mlbench}R Documentation

Benchmark Problem Friedman 3

Description

The regression problem Friedman 3 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 = atan ((x2 x3 - (1/(x2 x4)))/x1) + e

where e is N(0,sd).

Usage

mlbench.friedman3(n, sd=0.2)

Arguments

n number of patterns to create
sd Standard deviation of noise. The default value of 0.2 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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