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()
.
Usage
f_meas(data, ...)
# S3 method for 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(),
...
)
Arguments
- data
Either a
data.frame
containing the columns specified by thetruth
andestimate
arguments, or atable
/matrix
where the true class results should be in the columns of the table.- ...
Not currently used.
- truth
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, afactor
vector.- estimate
The column identifier for the predicted class results (that is also
factor
). As withtruth
this can be specified different ways but the primary method is to use an unquoted variable name. For_vec()
functions, afactor
vector.- beta
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.
- estimator
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 onestimate
.- na_rm
A
logical
value indicating whetherNA
values should be stripped before the computation proceeds.- case_weights
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()
, orhardhat::frequency_weights()
.- event_level
A single string. Either
"first"
or"second"
to specify which level oftruth
to consider as the "event". This argument is only applicable whenestimator = "binary"
. The default uses an internal helper that defaults to"first"
.
Value
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
).
Relevant Level
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.
Multiclass
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.
Implementation
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.
References
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
Examples
# 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