gg_var_imp: Visualize extracted overall feature importance
In BYUIDSS/HSPSModelR: Machine Learning Model Selection of Cognostic Data
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Generates ggplot of top ranked features
Usage
1
gg_var_imp(data, top_num = 20)
Arguments
data
tibble of results from HSPSModelR::var_imp_overall()
top_num
integer number of top features to display
Details
To be used after data is generated from HSPSModelR::var_imp_overall()
Value
tibble of ranked features including columns:
model
feature
Overall - Scaled score produced by caret::varImp()
rank - Ordered rank within each model
Author(s)
"Dallin Webb <dallinwebb@byui.edu>"
See Also
varImp
Examples
1
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8
## Not run:
vars <- var_imp_overall(models_list)
gg_var_imp(vars, top_num = 20)
## End(Not run)
BYUIDSS/HSPSModelR documentation built on May 10, 2019, 1:20 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Generates ggplot of top ranked features
Usage
1 | gg_var_imp(data, top_num = 20)
|
Arguments
data |
tibble of results from |
top_num |
|
Details
To be used after data is generated from HSPSModelR::var_imp_overall()
Value
tibble of ranked features including columns:
model
feature
Overall - Scaled score produced by
caret::varImp()rank - Ordered rank within each model
Author(s)
"Dallin Webb <dallinwebb@byui.edu>"
See Also
varImp
Examples
1 2 3 4 5 6 7 8 | ## Not run:
vars <- var_imp_overall(models_list)
gg_var_imp(vars, top_num = 20)
## End(Not run)
|
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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