my_rf_cv: Random forest cross-validation
In adammcbroom/STAT302package: Project 3 Package for STAT 302
Description
Usage
Arguments
Value
Examples
Description
This function uses a random forest algorithm with 100 trees on the
my_penguins data from the palmerpenguins package to predict
output body_mass_g with covariates bill_length_mm,
bill_depth_mm, and flipper_length_mm. Also computes the
cross-validation mean square error.
Usage
1
my_rf_cv(k)
Arguments
k
An integer representing the number of folds used in
cross-validation.
Value
Numeric representing the cross-validation error, or the average MSE
across all folds.
Examples
1
2
adammcbroom/STAT302package documentation built on Dec. 18, 2021, 10:25 p.m.
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Description Usage Arguments Value Examples
Description
This function uses a random forest algorithm with 100 trees on the
my_penguins data from the palmerpenguins package to predict
output body_mass_g with covariates bill_length_mm,
bill_depth_mm, and flipper_length_mm. Also computes the
cross-validation mean square error.
Usage
1 | my_rf_cv(k)
|
Arguments
k |
An integer representing the number of folds used in cross-validation. |
Value
Numeric representing the cross-validation error, or the average MSE across all folds.
Examples
1 2 |
R Package Documentation
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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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