These functions calculate the f_meas() of a measurement system for
finding relevant documents compared to reference results
(the truth regarding relevance). Highly related functions are recall()
and precision().
f_meas(data, ...)
# S3 method for class 'data.frame'
f_meas(
data,
truth,
estimate,
beta = 1,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)
f_meas_vec(
truth,
estimate,
beta = 1,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = yardstick_event_level(),
...
)Either a data.frame containing the columns specified by the
truth and estimate arguments, or a table/matrix where the true
class results should be in the columns of the table.
Not currently used.
The column identifier for the true class results
(that is a factor). This should be an unquoted column name although
this argument is passed by expression and supports
quasiquotation (you can unquote column
names). For _vec() functions, a factor vector.
The column identifier for the predicted class
results (that is also factor). As with truth this can be
specified different ways but the primary method is to use an
unquoted variable name. For _vec() functions, a factor vector.
A numeric value used to weight precision and recall. A value of 1 is traditionally used and corresponds to the harmonic mean of the two values but other values weight recall beta times more important than precision.
One of: "binary", "macro", "macro_weighted",
or "micro" to specify the type of averaging to be done. "binary" is
only relevant for the two class case. The other three are general methods
for calculating multiclass metrics. The default will automatically choose
"binary" or "macro" based on estimate.
A logical value indicating whether NA
values should be stripped before the computation proceeds.
The optional column identifier for case weights.
This should be an unquoted column name that evaluates to a numeric column
in data. For _vec() functions, a numeric vector,
hardhat::importance_weights(), or hardhat::frequency_weights().
A single string. Either "first" or "second" to specify
which level of truth to consider as the "event". This argument is only
applicable when estimator = "binary". The default uses an internal helper
that defaults to "first".
A tibble with columns .metric, .estimator,
and .estimate and 1 row of values.
For grouped data frames, the number of rows returned will be the same as the number of groups.
For f_meas_vec(), a single numeric value (or NA).
The measure "F" is a combination of precision and recall (see below).
There is no common convention on which factor level should
automatically be considered the "event" or "positive" result
when computing binary classification metrics. In yardstick, the default
is to use the first level. To alter this, change the argument
event_level to "second" to consider the last level of the factor the
level of interest. For multiclass extensions involving one-vs-all
comparisons (such as macro averaging), this option is ignored and
the "one" level is always the relevant result.
Macro, micro, and macro-weighted averaging is available for this metric.
The default is to select macro averaging if a truth factor with more
than 2 levels is provided. Otherwise, a standard binary calculation is done.
See vignette("multiclass", "yardstick") for more information.
Suppose a 2x2 table with notation:
| Reference | ||
| Predicted | Relevant | Irrelevant |
| Relevant | A | B |
| Irrelevant | C | D |
The formulas used here are:
$$recall = A/(A+C)$$ $$precision = A/(A+B)$$ $$F_{meas} = (1+\beta^2) * precision * recall/((\beta^2 * precision)+recall)$$
See the references for discussions of the statistics.
Buckland, M., & Gey, F. (1994). The relationship between Recall and Precision. Journal of the American Society for Information Science, 45(1), 12-19.
Powers, D. (2007). Evaluation: From Precision, Recall and F Factor to ROC, Informedness, Markedness and Correlation. Technical Report SIE-07-001, Flinders University
# Two class
data("two_class_example")
f_meas(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 f_meas binary 0.849
# Multiclass
library(dplyr)
data(hpc_cv)
hpc_cv %>%
filter(Resample == "Fold01") %>%
f_meas(obs, pred)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 f_meas macro 0.563
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
f_meas(obs, pred)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 f_meas macro 0.563
#> 2 Fold02 f_meas macro 0.542
#> 3 Fold03 f_meas macro 0.641
#> 4 Fold04 f_meas macro 0.593
#> 5 Fold05 f_meas macro 0.570
#> 6 Fold06 f_meas macro 0.554
#> 7 Fold07 f_meas macro 0.516
#> 8 Fold08 f_meas macro 0.601
#> 9 Fold09 f_meas macro 0.555
#> 10 Fold10 f_meas macro 0.560
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
f_meas(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 f_meas macro_weighted 0.696
#> 2 Fold02 f_meas macro_weighted 0.684
#> 3 Fold03 f_meas macro_weighted 0.739
#> 4 Fold04 f_meas macro_weighted 0.689
#> 5 Fold05 f_meas macro_weighted 0.692
#> 6 Fold06 f_meas macro_weighted 0.673
#> 7 Fold07 f_meas macro_weighted 0.646
#> 8 Fold08 f_meas macro_weighted 0.701
#> 9 Fold09 f_meas macro_weighted 0.652
#> 10 Fold10 f_meas macro_weighted 0.680
# Vector version
f_meas_vec(
two_class_example$truth,
two_class_example$predicted
)
#> [1] 0.8485981
# Making Class2 the "relevant" level
f_meas_vec(
two_class_example$truth,
two_class_example$predicted,
event_level = "second"
)
#> [1] 0.8258065