Compute the logarithmic loss of a classification model.

mn_log_loss(data, ...)

# S3 method for data.frame
mn_log_loss(data, truth, ..., na_rm = TRUE, sum = FALSE)

mn_log_loss_vec(truth, estimate, na_rm = TRUE, sum = FALSE, ...)



A data.frame containing the truth and estimate columns.


A set of unquoted column names or one or more dplyr selector functions to choose which variables contain the class probabilities. If truth is binary, only 1 column should be selected. Otherwise, there should be as many columns as factor levels of 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, a factor vector.


A logical value indicating whether NA values should be stripped before the computation proceeds.


A logical. Should the sum of the likelihood contributions be returned (instead of the mean value)?


If truth is binary, a numeric vector of class probabilities corresponding to the "relevant" class. Otherwise, a matrix with as many columns as factor levels of truth. It is assumed that these are in the same order as the levels of truth.


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


Log loss is a measure of the performance of a classification model. A perfect model has a log loss of 0.

Compared with accuracy(), log loss takes into account the uncertainty in the prediction and gives a more detailed view into the actual performance. For example, given two input probabilities of .6 and .9 where both are classified as predicting a positive value, say, "Yes", the accuracy metric would interpret them as having the same value. If the true output is "Yes", log loss penalizes .6 because it is "less sure" of it's result compared to the probability of .9.


Log loss has a known multiclass extension, and is simply the sum of the log loss values for each class prediction. Because of this, no averaging types are supported.

See also

Other class probability metrics: average_precision(), gain_capture(), pr_auc(), roc_auc(), roc_aunp(), roc_aunu()


# Two class data("two_class_example") mn_log_loss(two_class_example, truth, Class1)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 mn_log_loss binary 0.328
# Multiclass library(dplyr) data(hpc_cv) # You can use the col1:colN tidyselect syntax hpc_cv %>% filter(Resample == "Fold01") %>% mn_log_loss(obs, VF:L)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 mn_log_loss multiclass 0.734
# Groups are respected hpc_cv %>% group_by(Resample) %>% mn_log_loss(obs, VF:L)
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 mn_log_loss multiclass 0.734 #> 2 Fold02 mn_log_loss multiclass 0.808 #> 3 Fold03 mn_log_loss multiclass 0.705 #> 4 Fold04 mn_log_loss multiclass 0.747 #> 5 Fold05 mn_log_loss multiclass 0.799 #> 6 Fold06 mn_log_loss multiclass 0.766 #> 7 Fold07 mn_log_loss multiclass 0.927 #> 8 Fold08 mn_log_loss multiclass 0.855 #> 9 Fold09 mn_log_loss multiclass 0.861 #> 10 Fold10 mn_log_loss multiclass 0.821
# Vector version # Supply a matrix of class probabilities fold1 <- hpc_cv %>% filter(Resample == "Fold01") mn_log_loss_vec( truth = fold1$obs, matrix( c(fold1$VF, fold1$F, fold1$M, fold1$L), ncol = 4 ) )
#> [1] 0.7338423
# Supply `...` with quasiquotation prob_cols <- levels(two_class_example$truth) mn_log_loss(two_class_example, truth, Class1)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 mn_log_loss binary 0.328
mn_log_loss(two_class_example, truth, !! prob_cols[1])
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 mn_log_loss binary 0.328