# lift

0th

Percentile

##### Lift Plot

For classification models, this function creates a 'lift plot' that describes how well a model ranks samples for one class

Keywords
hplot
##### Usage
lift(x, ...)## S3 method for class 'formula':
lift(x, data = NULL, class = NULL,
subset = TRUE, lattice.options = NULL, labels = NULL,
...)## S3 method for class 'lift':
xyplot(x, data, plot = "gain", values = NULL, ...)
##### Arguments
x
a lattice formula (see xyplot for syntax) where the left-hand side of the formula is a factor class variable of the observed outcome and the right-hand side specifies one or model
data
For lift.formula, a data frame (or more precisely, anything that is a valid envir argument in eval, e.g., a list or an environment) containing values for any variables in the formula, as well as groups a
class
a character string for the class of interest
subset
An expression that evaluates to a logical or integer indexing vector. It is evaluated in data. Only the resulting rows of data are used for the plot.
lattice.options
A list that could be supplied to lattice.options
labels
A named list of labels for keys. The list should have an element for each term on the right-hand side of the formula and the names should match the names of the models.
plot
Either "gain" (the default) or "lift". The former plots the number of samples called events versus the event rate while the latter shows the event cut-off versus the lift statistic.
values
A vector of numbers between 0 and 100 specifying reference values for the percentage of samples found (i.e. the y-axis). Corresponding points on the x-axis are found via interpolation and line segments are shown to indicate how many samples must be tested
...
options to pass through to xyplot or the panel function (not used in lift.formula).
##### Details

lift.formula is used to process the data and xyplot.lift is used to create the plot.

To construct data for the the lift and gain plots, the following steps are used for each model:

1. The data are ordered by the numeric model prediction used on the right-hand side of the model formula
2. Each unique value of the score is treated as a cut point
3. The number of samples with true results equal toclassare determined
4. The lift is calculated as the ratio of the percentage of samples in each split corresponding toclassover the same percentage in the entire data set
lift with plot = "gain" produces a plot of the cumulative lift values by the percentage of samples evaluated while plot = "lift" shows the cut point value versus the lift statistic.

This implementation uses the lattice function xyplot, so plot elements can be changed via panel functions, trellis.par.set or other means. lift uses the panel function panel.lift2 by default, but it can be changes using update.trellis (see the examples in panel.lift2).

The following elements are set by default in the plot but can be changed by passing new values into xyplot.lift: xlab = "% Samples Tested", ylab = "% Samples Found", type = "S", ylim = extendrange(c(0, 100)) and xlim = extendrange(c(0, 100)).

##### Value

• lift.formula returns a list with elements:
• datathe data used for plotting
• cutsthe number of cuts
• classthe event class
• probNamesthe names of the model probabilities
• pctthe baseline event rate
• xyplot.lift returns a lattice object

xyplot, trellis.par.set

• lift
• lift.formula
• lift.default
• xyplot.lift
##### Examples
set.seed(1)
simulated <- data.frame(obs = factor(rep(letters[1:2], each = 100)),
perfect = sort(runif(200), decreasing = TRUE),
random = runif(200))

lift1 <- lift(obs ~ random, data = simulated)
lift1
xyplot(lift1)

lift2 <- lift(obs ~ random + perfect, data = simulated)
lift2
xyplot(lift2, auto.key = list(columns = 2))

xyplot(lift2, auto.key = list(columns = 2), value = c(10, 30))

xyplot(lift2, plot = "lift", auto.key = list(columns = 2))
Documentation reproduced from package caret, version 6.0-37, License: GPL-2

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