View source: R/compute_metrics.R

compute_metrics | R Documentation |

This function computes metrics from tuning results. The arguments and
output formats are closely related to those from `collect_metrics()`

, but
this function additionally takes a `metrics`

argument with a
metric set for new metrics to compute. This
allows for computing new performance metrics without requiring users to
re-evaluate models against resamples.

Note that the control option `save_pred = TRUE`

must
have been supplied when generating `x`

.

```
compute_metrics(x, metrics, summarize, event_level, ...)
## Default S3 method:
compute_metrics(x, metrics, summarize = TRUE, event_level = "first", ...)
## S3 method for class 'tune_results'
compute_metrics(x, metrics, ..., summarize = TRUE, event_level = "first")
```

`x` |
The results of a tuning function like |

`metrics` |
A metric set of new metrics to compute. See the "Details" section below for more information. |

`summarize` |
A single logical value indicating whether metrics should
be summarized over resamples ( |

`event_level` |
A single string containing either |

`...` |
Not currently used. |

Each metric in the set supplied to the `metrics`

argument must have a metric
type (usually `"numeric"`

, `"class"`

, or `"prob"`

) that matches some metric
evaluated when generating `x`

. e.g. For example, if `x`

was generated with
only hard `"class"`

metrics, this function can't compute metrics that take in
class probabilities (`"prob"`

.) By default, the tuning functions used to
generate `x`

compute metrics of all needed types.

A tibble. See `collect_metrics()`

for more details on the return value.

```
# load needed packages:
library(parsnip)
library(rsample)
library(yardstick)
# evaluate a linear regression against resamples.
# note that we pass `save_pred = TRUE`:
res <-
fit_resamples(
linear_reg(),
mpg ~ cyl + hp,
bootstraps(mtcars, 5),
control = control_grid(save_pred = TRUE)
)
# to return the metrics supplied to `fit_resamples()`:
collect_metrics(res)
# to compute new metrics:
compute_metrics(res, metric_set(mae))
# if `metrics` is the same as that passed to `fit_resamples()`,
# then `collect_metrics()` and `compute_metrics()` give the same
# output, though `compute_metrics()` is quite a bit slower:
all.equal(
collect_metrics(res),
compute_metrics(res, metric_set(rmse, rsq))
)
```

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