classification_cost() calculates the cost of a poor prediction based on
user-defined costs. The costs are multiplied by the estimated class
probabilities and the mean cost is returned.
classification_cost(data, ...)
# S3 method for class 'data.frame'
classification_cost(
data,
truth,
...,
costs = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL
)
classification_cost_vec(
truth,
estimate,
costs = NULL,
na_rm = TRUE,
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 data frame with columns "truth", "estimate", and "cost".
"truth" and "estimate" should be character columns containing unique
combinations of the levels of the truth factor.
"costs" should be a numeric column representing the cost that should
be applied when the "estimate" is predicted, but the true result is
"truth".
It is often the case that when "truth" == "estimate", the cost is zero
(no penalty for correct predictions).
If any combinations of the levels of truth are missing, their costs are
assumed to be zero.
If NULL, equal costs are used, applying a cost of 0 to correct
predictions, and a cost of 1 to incorrect predictions.
A logical value indicating whether NA
values should be stripped before the computation proceeds.
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 class_cost_vec(), a single numeric value (or NA).
As an example, suppose that there are three classes: "A", "B", and "C".
Suppose there is a truly "A" observation with class probabilities A = 0.3 / B = 0.3 / C = 0.4. Suppose that, when the true result is class "A", the
costs for each class were A = 0 / B = 5 / C = 10, penalizing the
probability of incorrectly predicting "C" more than predicting "B". The
cost for this prediction would be 0.3 * 0 + 0.3 * 5 + 0.4 * 10. This
calculation is done for each sample and the individual costs are averaged.
Other class probability metrics:
average_precision(),
brier_class(),
gain_capture(),
mn_log_loss(),
pr_auc(),
roc_auc(),
roc_aunp(),
roc_aunu()
library(dplyr)
# ---------------------------------------------------------------------------
# Two class example
data(two_class_example)
# Assuming `Class1` is our "event", this penalizes false positives heavily
costs1 <- tribble(
~truth, ~estimate, ~cost,
"Class1", "Class2", 1,
"Class2", "Class1", 2
)
# Assuming `Class1` is our "event", this penalizes false negatives heavily
costs2 <- tribble(
~truth, ~estimate, ~cost,
"Class1", "Class2", 2,
"Class2", "Class1", 1
)
classification_cost(two_class_example, truth, Class1, costs = costs1)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 classification_cost binary 0.288
classification_cost(two_class_example, truth, Class1, costs = costs2)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 classification_cost binary 0.260
# ---------------------------------------------------------------------------
# Multiclass
data(hpc_cv)
# Define cost matrix from Kuhn and Johnson (2013)
hpc_costs <- tribble(
~estimate, ~truth, ~cost,
"VF", "VF", 0,
"VF", "F", 1,
"VF", "M", 5,
"VF", "L", 10,
"F", "VF", 1,
"F", "F", 0,
"F", "M", 5,
"F", "L", 5,
"M", "VF", 1,
"M", "F", 1,
"M", "M", 0,
"M", "L", 1,
"L", "VF", 1,
"L", "F", 1,
"L", "M", 1,
"L", "L", 0
)
# You can use the col1:colN tidyselect syntax
hpc_cv %>%
filter(Resample == "Fold01") %>%
classification_cost(obs, VF:L, costs = hpc_costs)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 classification_cost multiclass 0.779
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
classification_cost(obs, VF:L, costs = hpc_costs)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 classification_cost multiclass 0.779
#> 2 Fold02 classification_cost multiclass 0.735
#> 3 Fold03 classification_cost multiclass 0.654
#> 4 Fold04 classification_cost multiclass 0.754
#> 5 Fold05 classification_cost multiclass 0.777
#> 6 Fold06 classification_cost multiclass 0.737
#> 7 Fold07 classification_cost multiclass 0.743
#> 8 Fold08 classification_cost multiclass 0.749
#> 9 Fold09 classification_cost multiclass 0.760
#> 10 Fold10 classification_cost multiclass 0.771