Description Usage Arguments Value References See Also Examples

Computes local or aggregate variable importance for a set of predictors from a fitted random forest object from the party, randomForest, randomForestSRC, or ranger package

1 | ```
variable_importance(fit, vars, interaction, nperm, data, ...)
``` |

`fit` |
object of class 'RandomForest', 'randomForest', 'rfsrc', or 'ranger' |

`vars` |
character, variables to find the importance of |

`interaction` |
logcal, compute the joint and additive importance for observations ( |

`nperm` |
positive integer giving the number of times to permute the indicated variables (default 10) |

`data` |
optional (unless using randomForest) data.frame with which to calculate importance |

`...` |
additional arguments to be passed to |

a named list of `vars`

with the return from `permutationImportance`

for each.

Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.

`plot_imp`

for plotting the results of `variable_importance`

.

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