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 data.frame
f_meas(data, truth, estimate, beta = 1, estimator = NULL, na_rm = TRUE, ...)

f_meas_vec(truth, estimate, beta = 1, estimator = NULL, na_rm = TRUE, ...)



Either a data.frame containing the truth and estimate columns, 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.


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).

Relevant Level

There is no common convention on which factor level should automatically be considered the "event" or "positive" result. In yardstick, the default is to use the first level. To change this, a global option called yardstick.event_first is set to TRUE when the package is loaded. This can be changed to FALSE if the last level of the factor is considered the level of interest by running: options(yardstick.event_first = FALSE). 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:


The formulas used here are:

$$recall = A/(A+C)$$ $$precision = A/(A+B)$$ $$F_meas_\beta = (1+\beta^2) * precision * recall/((\beta^2 * precision)+recall)$$

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

See also

Other class metrics: accuracy(), bal_accuracy(), detection_prevalence(), j_index(), kap(), mcc(), npv(), ppv(), precision(), recall(), sens(), spec()

Other relevance metrics: precision(), recall()


# Two class data("two_class_example") f_meas(two_class_example, truth, predicted)
#> # A tibble: 1 x 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 x 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 x 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 x 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 options(yardstick.event_first = FALSE) f_meas_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.8258065
options(yardstick.event_first = TRUE)