View source: R/prob-gain_curve.R
gain_curve | R Documentation |
gain_curve()
constructs the full gain curve and returns a tibble. See
gain_capture()
for the relevant area under the gain curve. Also see
lift_curve()
for a closely related concept.
gain_curve(data, ...)
## S3 method for class 'data.frame'
gain_curve(
data,
truth,
...,
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 |
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 |
There is a ggplot2::autoplot()
method for quickly visualizing the curve.
This works for binary and multiclass output, and also works with grouped data
(i.e. from resamples). See the examples.
The greater the area between the gain curve and the baseline, the better the model.
Gain curves are identical to CAP curves (cumulative accuracy profile). See the Engelmann reference for more information on CAP curves.
A tibble with class gain_df
or gain_grouped_df
having columns:
.n
The index of the current sample.
.n_events
The index of the current unique sample. Values with repeated
estimate
values are given identical indices in this column.
.percent_tested
The cumulative percentage of values tested.
.percent_found
The cumulative percentage of true results relative to the
total number of true results.
If using the case_weights
argument, all of the above columns will be
weighted. This makes the most sense with frequency weights, which are integer
weights representing the number of times a particular observation should be
repeated.
The motivation behind cumulative gain and lift charts is as a visual method to
determine the effectiveness of a model when compared to the results one
might expect without a model. As an example, without a model, if you were
to advertise to a random 10% of your customer base, then you might expect
to capture 10% of the of the total number of positive responses had you
advertised to your entire customer base. Given a model that predicts
which customers are more likely to respond, the hope is that you can more
accurately target 10% of your customer base and capture
>
10% of the total number of positive responses.
The calculation to construct gain curves is as follows:
truth
and estimate
are placed in descending order by the estimate
values (estimate
here is a single column supplied in ...
).
The cumulative number of samples with true results relative to the entire number of true results are found. This is the y-axis in a gain chart.
If a multiclass truth
column is provided, a one-vs-all
approach will be taken to calculate multiple curves, one per level.
In this case, there will be an additional column, .level
,
identifying the "one" column in the one-vs-all calculation.
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.
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.
Compute the relevant area under the gain curve with gain_capture()
.
Other curve metrics:
lift_curve()
,
pr_curve()
,
roc_curve()
# ---------------------------------------------------------------------------
# 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_curve(two_class_example, truth, Class1)
# ---------------------------------------------------------------------------
# `autoplot()`
library(ggplot2)
library(dplyr)
# Use autoplot to visualize
# The top left hand corner of the grey triangle is a "perfect" gain curve
autoplot(gain_curve(two_class_example, truth, Class1))
# Multiclass one-vs-all approach
# One curve per level
hpc_cv %>%
filter(Resample == "Fold01") %>%
gain_curve(obs, VF:L) %>%
autoplot()
# Same as above, but will all of the resamples
# The resample with the minimum (farthest to the left) "perfect" value is
# used to draw the shaded region
hpc_cv %>%
group_by(Resample) %>%
gain_curve(obs, VF:L) %>%
autoplot()
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