f_model_importance_randomForest: extract variable importance for randomForest model
In erblast/oetteR: Collection of personal R functions
Description
Usage
Arguments
Value
Examples
Description
extract variable importance for randomForest model
Usage
1
Arguments
m
model of class randomForest
Value
dataframe
Examples
1
2
3
4
5
6
7
8
#regression
m = randomForest::randomForest(mtcars, disp~.)
f_model_importance_randomForest(m)
#classification
data_ls = f_clean_data(mtcars)
m = randomForest::randomForest(data_ls$data, cyl~.)
f_model_importance_randomForest(m)
erblast/oetteR documentation built on May 27, 2019, 12:11 p.m.
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Description Usage Arguments Value Examples
Description
extract variable importance for randomForest model
Usage
1 |
Arguments
m |
model of class randomForest |
Value
dataframe
Examples
1 2 3 4 5 6 7 8 | #regression
m = randomForest::randomForest(mtcars, disp~.)
f_model_importance_randomForest(m)
#classification
data_ls = f_clean_data(mtcars)
m = randomForest::randomForest(data_ls$data, cyl~.)
f_model_importance_randomForest(m)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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