View source: R/prob-gain_capture.R
gain_capture | R Documentation |
gain_capture()
is a measure of performance similar to an AUC calculation,
but applied to a gain curve.
gain_capture(data, ...)
## S3 method for class 'data.frame'
gain_capture(
data,
truth,
...,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL
)
gain_capture_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL,
...
)
data |
A |
... |
A set of unquoted column names or one or more
|
truth |
The column identifier for the true class results
(that is a |
estimator |
One of |
na_rm |
A |
event_level |
A single string. Either |
case_weights |
The optional column identifier for case weights.
This should be an unquoted column name that evaluates to a numeric column
in |
estimate |
If |
gain_capture()
calculates the area under the gain curve, but above
the baseline, and then divides that by the area under a perfect gain curve,
but above the baseline. It is meant to represent the amount of potential
gain "captured" by the model.
The gain_capture()
metric is identical to the accuracy ratio (AR), which
is also sometimes called the gini coefficient. These two are generally
calculated on a cumulative accuracy profile curve, but this is the same as
a gain curve. See the Engelmann reference for more information.
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 gain_capture_vec()
, a single numeric
value (or NA
).
There is no common convention on which factor level should
automatically be considered the "event" or "positive" result
when computing binary classification metrics. In yardstick
, the default
is to use the first level. To alter this, change the argument
event_level
to "second"
to consider the last level of the factor the
level of interest. For multiclass extensions involving one-vs-all
comparisons (such as macro averaging), this option is ignored and
the "one" level is always the relevant result.
Macro and macro-weighted averaging is available for this metric.
The default is to select macro averaging if a truth
factor with more
than 2 levels is provided. Otherwise, a standard binary calculation is done.
See vignette("multiclass", "yardstick")
for more information.
Max Kuhn
Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk (2003). "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank.
gain_curve()
to compute the full gain curve.
Other class probability metrics:
average_precision()
,
brier_class()
,
classification_cost()
,
mn_log_loss()
,
pr_auc()
,
roc_auc()
,
roc_aunp()
,
roc_aunu()
# ---------------------------------------------------------------------------
# Two class example
# `truth` is a 2 level factor. The first level is `"Class1"`, which is the
# "event of interest" by default in yardstick. See the Relevant Level
# section above.
data(two_class_example)
# Binary metrics using class probabilities take a factor `truth` column,
# and a single class probability column containing the probabilities of
# the event of interest. Here, since `"Class1"` is the first level of
# `"truth"`, it is the event of interest and we pass in probabilities for it.
gain_capture(two_class_example, truth, Class1)
# ---------------------------------------------------------------------------
# Multiclass example
# `obs` is a 4 level factor. The first level is `"VF"`, which is the
# "event of interest" by default in yardstick. See the Relevant Level
# section above.
data(hpc_cv)
# You can use the col1:colN tidyselect syntax
library(dplyr)
hpc_cv %>%
filter(Resample == "Fold01") %>%
gain_capture(obs, VF:L)
# Change the first level of `obs` from `"VF"` to `"M"` to alter the
# event of interest. The class probability columns should be supplied
# in the same order as the levels.
hpc_cv %>%
filter(Resample == "Fold01") %>%
mutate(obs = relevel(obs, "M")) %>%
gain_capture(obs, M, VF:L)
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
gain_capture(obs, VF:L)
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
gain_capture(obs, VF:L, estimator = "macro_weighted")
# Vector version
# Supply a matrix of class probabilities
fold1 <- hpc_cv %>%
filter(Resample == "Fold01")
gain_capture_vec(
truth = fold1$obs,
matrix(
c(fold1$VF, fold1$F, fold1$M, fold1$L),
ncol = 4
)
)
# ---------------------------------------------------------------------------
# Visualize gain_capture()
# Visually, this represents the area under the black curve, but above the
# 45 degree line, divided by the area of the shaded triangle.
library(ggplot2)
autoplot(gain_curve(two_class_example, truth, Class1))
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