# Efficient grid search via racing with win/loss statistics

Source:`R/tune_race_win_loss.R`

`tune_race_win_loss.Rd`

`tune_race_win_loss()`

computes a set of performance metrics (e.g. accuracy or RMSE)
for a pre-defined set of tuning parameters that correspond to a model or
recipe across one or more resamples of the data. After an initial number of
resamples have been evaluated, the process eliminates tuning parameter
combinations that are unlikely to be the best results using a statistical
model. For each pairwise combinations of tuning parameters, win/loss
statistics are calculated and a logistic regression model is used to measure
how likely each combination is to win overall.

## Usage

```
tune_race_win_loss(object, ...)
# S3 method for model_spec
tune_race_win_loss(
object,
preprocessor,
resamples,
...,
param_info = NULL,
grid = 10,
metrics = NULL,
control = control_race()
)
# S3 method for workflow
tune_race_win_loss(
object,
resamples,
...,
param_info = NULL,
grid = 10,
metrics = NULL,
control = control_race()
)
```

## Arguments

- object
A

`parsnip`

model specification or a`workflows::workflow()`

.- ...
Not currently used. The technical details of this method are described in Kuhn (2014).

Racing methods are efficient approaches to grid search. Initially, the function evaluates all tuning parameters on a small initial set of resamples. The

`burn_in`

argument of`control_race()`

sets the number of initial resamples.The performance statistics from the current set of resamples are converted to win/loss/tie results. For example, for two parameters (

`j`

and`k`

) in a classification model that have each been resampled three times:`| area under the ROC curve | ----------------------------- resample | parameter j | parameter k | winner --------------------------------------------- 1 | 0.81 | 0.92 | k 2 | 0.95 | 0.94 | j 3 | 0.79 | 0.81 | k ---------------------------------------------`

After the third resample, parameter

`k`

has a 2:1 win/loss ratio versus`j`

. Parameters with equal results are treated as a half-win for each setting. These statistics are determined for all pairwise combinations of the parameters and a Bradley-Terry model is used to model these win/loss/tie statistics. This model can compute the ability of a parameter combination to win overall. A confidence interval for the winning ability is computed and any settings whose interval includes zero are retained for future resamples (since it is not statistically different form the best results).The next resample is used with the remaining parameter combinations and the statistical analysis is updated. More candidate parameters may be excluded with each new resample that is processed.

The

`control_race()`

function contains are parameter for the significance cutoff applied to the Bradley-Terry model results as well as other relevant arguments.- preprocessor
A traditional model formula or a recipe created using

`recipes::recipe()`

.- resamples
An

`rset()`

object that has multiple resamples (i.e., is not a validation set).- param_info
A

`dials::parameters()`

object or`NULL`

. If none is given, a parameters set is derived from other arguments. Passing this argument can be useful when parameter ranges need to be customized.- grid
A data frame of tuning combinations or a positive integer. The data frame should have columns for each parameter being tuned and rows for tuning parameter candidates. An integer denotes the number of candidate parameter sets to be created automatically.

- metrics
A

`yardstick::metric_set()`

or`NULL`

.- control
An object used to modify the tuning process.

## Value

An object with primary class `tune_race`

in the same standard format
as objects produced by `tune::tune_grid()`

.

## References

Kuhn, M 2014. "Futility Analysis in the Cross-Validation of Machine Learning Models." https://arxiv.org/abs/1405.6974.

## Examples

```
# \donttest{
library(parsnip)
library(rsample)
library(dials)
## -----------------------------------------------------------------------------
if (rlang::is_installed(c("discrim", "modeldata"))) {
library(discrim)
data(two_class_dat, package = "modeldata")
set.seed(6376)
rs <- bootstraps(two_class_dat, times = 10)
## -----------------------------------------------------------------------------
# optimize an regularized discriminant analysis model
rda_spec <-
discrim_regularized(frac_common_cov = tune(), frac_identity = tune()) %>%
set_engine("klaR")
## -----------------------------------------------------------------------------
ctrl <- control_race(verbose_elim = TRUE)
set.seed(11)
grid_wl <-
rda_spec %>%
tune_race_win_loss(Class ~ ., resamples = rs, grid = 10, control = ctrl)
# Shows only the fully resampled parameters
show_best(grid_wl, metric = "roc_auc")
plot_race(grid_wl)
}
#> ℹ Racing will maximize the roc_auc metric.
#> ℹ Resamples are analyzed in a random order.
#> ℹ Bootstrap05: 1 eliminated; 9 candidates remain.
#> ℹ Bootstrap07: 1 eliminated; 8 candidates remain.
#> ℹ Bootstrap10: 1 eliminated; 7 candidates remain.
#> ℹ Bootstrap01: 1 eliminated; 6 candidates remain.
#> ℹ Bootstrap08: 1 eliminated; 5 candidates remain.
#> ℹ Bootstrap03: 1 eliminated; 4 candidates remain.
#> ℹ Bootstrap09: 1 eliminated; 3 candidates remain.
# }
```