randomMachines (version 0.1.1)

sim_reg1: 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)

Value

A simulated data.frame with two predictors and the target variable.

Arguments

n

Sample size

sigma

Standard deviation of residual noise

Author

Mateus Maia: , Anderson Ara:

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)