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MulvariateRandomForestVarImp: Variable Importance Measures for Multivariate Random Forests
Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.
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Package details |
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| Author | Sikdar Sharmistha [aut], Hooker Giles [aut], Kadiyali Vrinda [ctb], Dogonadze Nika [cre] |
| Maintainer | Dogonadze Nika <nika.dogonadze@toptal.com> |
| License | GPL (>= 3) |
| Version | 0.0.2 |
| URL | https://github.com/Megatvini/VIM/ |
| Package repository | View on CRAN |
| Installation |
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