Lift curve

Description

lift_curve() constructs the full lift curve and returns a tibble. See gain_curve() for a closely related concept.

Usage

lift_curve(data, ...)

## S3 method for class 'data.frame'
lift_curve(
  data,
  truth,
  ...,
  na_rm = TRUE,
  event_level = yardstick_event_level()
)

autoplot.lift_df(object, ...)

Arguments

Details

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.

Value

A tibble with class lift_df or lift_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.

• .lift - First calculate the cumulative percentage of true results relative to the total number of true results. Then divide that by .percent_tested.

Gain and Lift Curves

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\ to capture 10\ 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\ \>10\

The calculation to construct lift curves is as follows:

1. truth and estimate are placed in descending order by the estimate values (estimate here is a single column supplied in ...).

2. The cumulative number of samples with true results relative to the entire number of true results are found.

3. The cumulative \ 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.

Multiclass

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.

Relevant Level

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.

Author(s)

Max Kuhn

See Also

Other curve metrics: gain_curve(), pr_curve(), roc_curve()

Examples

# ---------------------------------------------------------------------------
# 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)

# ---------------------------------------------------------------------------
# `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()