show_best | R Documentation |

`show_best()`

displays the top sub-models and their performance estimates.

```
show_best(x, ...)
## Default S3 method:
show_best(x, ...)
## S3 method for class 'tune_results'
show_best(x, metric = NULL, n = 5, ...)
select_best(x, ...)
## Default S3 method:
select_best(x, ...)
## S3 method for class 'tune_results'
select_best(x, metric = NULL, ...)
select_by_pct_loss(x, ...)
## Default S3 method:
select_by_pct_loss(x, ...)
## S3 method for class 'tune_results'
select_by_pct_loss(x, ..., metric = NULL, limit = 2)
select_by_one_std_err(x, ...)
## Default S3 method:
select_by_one_std_err(x, ...)
## S3 method for class 'tune_results'
select_by_one_std_err(x, ..., metric = NULL)
```

`x` |
The results of |

`...` |
For |

`metric` |
A character value for the metric that will be used to sort
the models. (See
https://yardstick.tidymodels.org/articles/metric-types.html for
more details). Not required if a single metric exists in |

`n` |
An integer for the number of top results/rows to return. |

`limit` |
The limit of loss of performance that is acceptable (in percent units). See details below. |

`select_best()`

finds the tuning parameter combination with the best
performance values.

`select_by_one_std_err()`

uses the "one-standard error rule" (Breiman _el
at, 1984) that selects the most simple model that is within one standard
error of the numerically optimal results.

`select_by_pct_loss()`

selects the most simple model whose loss of
performance is within some acceptable limit.

For percent loss, suppose the best model has an RMSE of 0.75 and a simpler
model has an RMSE of 1. The percent loss would be `(1.00 - 0.75)/1.00 * 100`

,
or 25 percent. Note that loss will always be non-negative.

A tibble with columns for the parameters. `show_best()`

also
includes columns for performance metrics.

Breiman, Leo; Friedman, J. H.; Olshen, R. A.; Stone, C. J. (1984).
*Classification and Regression Trees.* Monterey, CA: Wadsworth.

```
data("example_ames_knn")
show_best(ames_iter_search, metric = "rmse")
select_best(ames_iter_search, metric = "rsq")
# To find the least complex model within one std error of the numerically
# optimal model, the number of nearest neighbors are sorted from the largest
# number of neighbors (the least complex class boundary) to the smallest
# (corresponding to the most complex model).
select_by_one_std_err(ames_grid_search, metric = "rmse", desc(K))
# Now find the least complex model that has no more than a 5% loss of RMSE:
select_by_pct_loss(
ames_grid_search,
metric = "rmse",
limit = 5, desc(K)
)
```

tune documentation built on Aug. 24, 2023, 1:09 a.m.

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