| sim_reg1 {randomMachines} | R Documentation |
Simulation for a regression toy examples from Random Machines Regression 1
Description
Simulation toy example initially found in Scornet (2016), and used and escribed by Ara et. al (2022).
Inputs are 2 independent variables uniformly distributed on the interval [-1,1]. Outputs are generated following the equation
Y={X^{2}_{1}}+e^{{-{X^{2}_{2}}}} + \varepsilon, \varepsilon \sim \mathcal{N}(0,\sigma^{2})
Usage
sim_reg1(n, sigma)
Arguments
n |
Sample size |
sigma |
Standard deviation of residual noise |
Value
A simulated data.frame with two predictors and the target variable.
Author(s)
Mateus Maia: mateusmaia11@gmail.com, Anderson Ara: ara@ufpr.br
References
Ara, Anderson, et al. "Regression random machines: An ensemble support vector regression model with free kernel choice." Expert Systems with Applications 202 (2022): 117107.
Scornet, E. (2016). Random forests and kernel methods. IEEE Transactions on Information Theory, 62(3), 1485-1500.
Examples
library(randomMachines)
sim_data <- sim_reg1(n=100)