loo_cv: Leave-One-Out Cross-Validation
In tidymodels/rsample: General Resampling Infrastructure
loo_cv R Documentation
Leave-One-Out Cross-Validation
Description
Leave-one-out (LOO) cross-validation uses one data point in the original
set as the assessment data and all other data points as the analysis set. A
LOO resampling set has as many resamples as rows in the original data set.
Usage
loo_cv(data, ...)
Arguments
data
A data frame.
...
These dots are for future extensions and must be empty.
Value
An tibble with classes loo_cv, rset, tbl_df, tbl, and
data.frame. The results include a column for the data split objects and
one column called id that has a character string with the resample
identifier.
Examples
loo_cv(mtcars)
tidymodels/rsample documentation built on Sept. 29, 2024, 10:48 p.m.
R Package Documentation
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| loo_cv | R Documentation |
Leave-One-Out Cross-Validation
Description
Leave-one-out (LOO) cross-validation uses one data point in the original set as the assessment data and all other data points as the analysis set. A LOO resampling set has as many resamples as rows in the original data set.
Usage
loo_cv(data, ...)
Arguments
data |
A data frame. |
... |
These dots are for future extensions and must be empty. |
Value
An tibble with classes loo_cv, rset, tbl_df, tbl, and
data.frame. The results include a column for the data split objects and
one column called id that has a character string with the resample
identifier.
Examples
loo_cv(mtcars)
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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