select_fea_xgb: Feature selection using xgboost
In woobe/predictr: My favourite routines for developing and evaluating predictive models in R
Description
Usage
Arguments
Examples
Description
Feature selection using xgboost
Usage
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select_fea_xgb(x, y, n_p = 1, t_obj = "multi:softprob",
t_eval = "mlogloss", n_class = NULL, n_depth = 9, n_eta = 0.1,
n_subsamp = 0.9, n_core = 8, n_round = 100, n_cv_fold = 3,
n_final_cv_fold = 10, n_size = "auto", n_repeat = 3, verbose = TRUE)
Arguments
x
predictors
y
target
Examples
1
## T.B.A.
woobe/predictr documentation built on May 4, 2019, 9:47 a.m.
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Description Usage Arguments Examples
Description
Feature selection using xgboost
Usage
1 2 3 4 | select_fea_xgb(x, y, n_p = 1, t_obj = "multi:softprob",
t_eval = "mlogloss", n_class = NULL, n_depth = 9, n_eta = 0.1,
n_subsamp = 0.9, n_core = 8, n_round = 100, n_cv_fold = 3,
n_final_cv_fold = 10, n_size = "auto", n_repeat = 3, verbose = TRUE)
|
Arguments
x |
predictors |
y |
target |
Examples
1 | ## T.B.A.
|
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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