select_features: Feature Selection using Caret's Random Forest Approach
In woobe/predictr: My favourite routines for developing and evaluating predictive models in R
Description
Usage
Arguments
Examples
Description
This function determines the best combination of predictors.
Usage
1
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3
select_features(x, y, n_core = 8, n_cv_fold = 10, n_cv_repeat = 1,
n_run = 20, n_maxsamp = 1000, n_size = c(2:10), n_cutoff = 0.6,
verbose = TRUE)
Arguments
x
a data frame or matrix of predictors (x).
y
target (numeric or factor)
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
This function determines the best combination of predictors.
Usage
1 2 3 | select_features(x, y, n_core = 8, n_cv_fold = 10, n_cv_repeat = 1,
n_run = 20, n_maxsamp = 1000, n_size = c(2:10), n_cutoff = 0.6,
verbose = TRUE)
|
Arguments
x |
a data frame or matrix of predictors (x). |
y |
target (numeric or factor) |
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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