lift_curve() constructs the full lift curve and returns a tibble. See
gain_curve() for a closely related concept.
lift_curve(data, ...) # S3 method for data.frame lift_curve( data, truth, ..., na_rm = TRUE, event_level = yardstick_event_level(), case_weights = NULL )
data.framecontaining the columns specified by
A set of unquoted column names or one or more
dplyrselector functions to choose which variables contain the class probabilities. If
truthis binary, only 1 column should be selected. Otherwise, there should be as many columns as factor levels of
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
logicalvalue indicating whether
NAvalues should be stripped before the computation proceeds.
A single string. Either
"second"to specify which level of
truthto consider as the "event". This argument is only applicable when
estimator = "binary". The default uses an internal helper that generally defaults to
"first", however, if the deprecated global option
yardstick.event_firstis set, that will be used instead with a warning.
The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in
_vec()functions, a numeric vector.
A tibble with class
.nThe index of the current sample.
.n_eventsThe index of the current unique sample. Values with repeated
estimatevalues are given identical indices in this column.
.percent_testedThe cumulative percentage of values tested.
.liftFirst calculate the cumulative percentage of true results relative to the total number of true results. Then divide that by
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
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 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 lift curves is as follows:
estimateare placed in descending order by the
estimatehere is a single column supplied in
The cumulative number of samples with true results relative to the entire number of true results are found.
%found is divided by the cumulative
%tested to construct the lift value. This ratio represents the factor of improvement over an uninformed model. Values
>1 represent a valuable model. This is the y-axis of the lift 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,
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
"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.
# --------------------------------------------------------------------------- # 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. lift_curve(two_class_example, truth, Class1) #> # A tibble: 501 × 4 #> .n .n_events .percent_tested .lift #> <dbl> <dbl> <dbl> <dbl> #> 1 0 0 0 NaN #> 2 1 1 0.2 1.94 #> 3 2 2 0.4 1.94 #> 4 3 3 0.6 1.94 #> 5 4 4 0.8 1.94 #> 6 5 5 1 1.94 #> 7 6 6 1.2 1.94 #> 8 7 7 1.4 1.94 #> 9 8 8 1.6 1.94 #> 10 9 9 1.8 1.94 #> # … with 491 more rows # --------------------------------------------------------------------------- # `autoplot()` library(ggplot2) library(dplyr) # Use autoplot to visualize autoplot(lift_curve(two_class_example, truth, Class1)) # Multiclass one-vs-all approach # One curve per level hpc_cv %>% filter(Resample == "Fold01") %>% lift_curve(obs, VF:L) %>% autoplot() # Same as above, but will all of the resamples hpc_cv %>% group_by(Resample) %>% lift_curve(obs, VF:L) %>% autoplot()