These functions calculate the precision() of a measurement system for finding relevant documents compared to reference results (the truth regarding relevance). Highly related functions are recall() and f_meas().

precision(data, ...)

# S3 method for data.frame
precision(data, truth, estimate, estimator = NULL, na_rm = TRUE, ...)

precision_vec(truth, estimate, 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.


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 precision_vec(), a single numeric value (or NA).


The precision is the percentage of predicted truly relevant results of the total number of predicted relevant results and characterizes the "purity in retrieval performance" (Buckland and Gey, 1994).

When the denominator of the calculation is 0, precision is undefined. This happens when both # true_positive = 0 and # false_positive = 0 are true, which mean that there were no predicted events. When computing binary precision, a NA value will be returned with a warning. When computing multiclass precision, the individual NA values will be removed, and the computation will procede, with a warning.

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(), f_meas(), j_index(), kap(), mcc(), npv(), ppv(), recall(), sens(), spec()

Other relevance metrics: f_meas(), recall()


# Two class data("two_class_example") precision(two_class_example, truth, predicted)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 precision binary 0.819
# Multiclass library(dplyr) data(hpc_cv) hpc_cv %>% filter(Resample == "Fold01") %>% precision(obs, pred)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 precision macro 0.637
# Groups are respected hpc_cv %>% group_by(Resample) %>% precision(obs, pred)
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 precision macro 0.637 #> 2 Fold02 precision macro 0.603 #> 3 Fold03 precision macro 0.706 #> 4 Fold04 precision macro 0.658 #> 5 Fold05 precision macro 0.651 #> 6 Fold06 precision macro 0.626 #> 7 Fold07 precision macro 0.562 #> 8 Fold08 precision macro 0.652 #> 9 Fold09 precision macro 0.605 #> 10 Fold10 precision macro 0.625
# Weighted macro averaging hpc_cv %>% group_by(Resample) %>% precision(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 precision macro_weighted 0.697 #> 2 Fold02 precision macro_weighted 0.690 #> 3 Fold03 precision macro_weighted 0.752 #> 4 Fold04 precision macro_weighted 0.690 #> 5 Fold05 precision macro_weighted 0.705 #> 6 Fold06 precision macro_weighted 0.682 #> 7 Fold07 precision macro_weighted 0.649 #> 8 Fold08 precision macro_weighted 0.702 #> 9 Fold09 precision macro_weighted 0.661 #> 10 Fold10 precision macro_weighted 0.683
# Vector version precision_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.8194946
# Making Class2 the "relevant" level options(yardstick.event_first = FALSE) precision_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.8609865
options(yardstick.event_first = TRUE)