Compute the logarithmic loss of a classification model.
mn_log_loss(data, ...)
# S3 method for class 'data.frame'
mn_log_loss(
data,
truth,
...,
na_rm = TRUE,
sum = FALSE,
event_level = yardstick_event_level(),
case_weights = NULL
)
mn_log_loss_vec(
truth,
estimate,
na_rm = TRUE,
sum = FALSE,
event_level = yardstick_event_level(),
case_weights = NULL,
...
)A data.frame containing the columns specified by truth and
....
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,
and it should correspond to the value of event_level. Otherwise, there
should be as many columns as factor levels of truth and the ordering of
the columns should be the same as the 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)?
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".
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().
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 its 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.
Other class probability metrics:
average_precision(),
brier_class(),
classification_cost(),
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 × 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 × 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 × 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 × 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 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 mn_log_loss binary 0.328