mlbench.friedman3

The regression problem Friedman 3 as described in Friedman (1991) and
Breiman (1996). Inputs are 4 independent variables uniformly
distrtibuted over the ranges
$$0 \le x1 \le 100$$
$$40 \pi \le x2 \le 560 \pi$$
$$0 \le x3 \le 1$$
$$1 \le x4 \le 11$$
The outputs are created according to the formula
$$y = \mbox{atan}((x2 x3 - (1/(x2 x4)))/x1) + e$$
where e is N(0,sd).

datagen

A collection of artificial and real-world machine learning
benchmark problems, including, e.g., several
data sets from the UCI repository.

Kurt Hornik

mlbench

Machine Learning Benchmark Problems

Friedrich Leisch

Evgenia Dimitriadou

mlbench.friedman3 function

<dl><dt>n</dt>
<dd>number of patterns to create</dd><dt>sd</dt>
<dd>Standard deviation of noise. The default value of 0.1 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.</dd></dl>

Arguments

Benchmark Problem Friedman 3 — mlbench.friedman3

<dl>

<dt>n</dt>
<dd>number of patterns to create</dd>

<dt>sd</dt>
<dd>Standard deviation of noise. The default value of 0.1 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.</dd>

</dl>

mlbench.friedman3: Benchmark Problem Friedman 3

Description

Usage

Value

Arguments

References