Mean log loss for multinomial data

Compute the logarithmic loss of a classification model.

Usage

mn_log_loss(data, ...)

# S3 method for 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,
  ...
)

Arguments

data

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.

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.

na_rm

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

sum

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

event_level

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".

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(), or hardhat::frequency_weights().

estimate

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.

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

Details

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.

Multiclass

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(), brier_class(), classification_cost(), gain_capture(), pr_auc(), roc_auc(), roc_aunp(), roc_aunu()

Author

Max Kuhn

Examples

# 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