rminer (version 1.4.6)

sa_fri1: Synthetic regression and classification datasets for measuring input importance of supervised learning models

Description

5 Synthetic regression (sa_fri1, sa_ssin, sa_psin, sa_int2, sa_tree) and 4 classification (sa_ssin_2, sa_ssin_n2p, sa_int2_3c, sa_int2_8p) datasets for measuring input importance of supervised learning models

Usage

data(sa_fri1)

Format

A data frame with 1000 observations on the following variables.

xn

input (numeric or factor, depends on the dataset)

y

output target (numeric or factor, depends on the dataset)

Details

Check reference or source for full details

References

• To cite the Importance function, sensitivity analysis methods or synthetic datasets, please use: P. Cortez and M.J. Embrechts. Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models. In Information Sciences, Elsevier, 225:1-17, March 2013. http://dx.doi.org/10.1016/j.ins.2012.10.039

Examples

# NOT RUN {
data(sa_ssin)
print(summary(sa_ssin))
# }
# NOT RUN {
plot(sa_ssin$x1,sa_ssin$y)
# }