Compute the Brier score for a classification model.
brier_class(data, ...)
# S3 method for class 'data.frame'
brier_class(data, truth, ..., na_rm = TRUE, case_weights = NULL)
brier_class_vec(truth, estimate, na_rm = TRUE, 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.
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 brier_class_vec(), a single numeric value (or NA).
The Brier score is analogous to the mean squared error in regression models. The difference between a binary indicator for a class and its corresponding class probability are squared and averaged.
This function uses the convention in Kruppa et al (2014) and divides the result by two.
Smaller values of the score are associated with better model performance.
Brier scores can be computed in the same way for any number of classes. Because of this, no averaging types are supported.
Kruppa, J., Liu, Y., Diener, H.-C., Holste, T., Weimar, C., Koonig, I. R., and Ziegler, A. (2014) Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications. Biometrical Journal, 56 (4): 564-583.
Other class probability metrics:
average_precision(),
classification_cost(),
gain_capture(),
mn_log_loss(),
pr_auc(),
roc_auc(),
roc_aunp(),
roc_aunu()
# Two class
data("two_class_example")
brier_class(two_class_example, truth, Class1)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 brier_class binary 0.106
# Multiclass
library(dplyr)
data(hpc_cv)
# You can use the col1:colN tidyselect syntax
hpc_cv %>%
filter(Resample == "Fold01") %>%
brier_class(obs, VF:L)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 brier_class multiclass 0.202
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
brier_class(obs, VF:L)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 brier_class multiclass 0.202
#> 2 Fold02 brier_class multiclass 0.215
#> 3 Fold03 brier_class multiclass 0.177
#> 4 Fold04 brier_class multiclass 0.204
#> 5 Fold05 brier_class multiclass 0.213
#> 6 Fold06 brier_class multiclass 0.214
#> 7 Fold07 brier_class multiclass 0.221
#> 8 Fold08 brier_class multiclass 0.209
#> 9 Fold09 brier_class multiclass 0.235
#> 10 Fold10 brier_class multiclass 0.218