lift
From caret v5.04-007
by Max Kuhn
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, data = NULL, class = NULL, cuts = 11,
subset = TRUE, lattice.options = NULL,
ylabel = "% Samples Found", xlabel = "% Samples Tested",
...)
Arguments
- x
- a
lattice
formula (seexyplot
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
- a data frame (or more precisely, anything that is a valid
envir
argument inveal
, e.g., a list or an environment) containing values for any variables in the formula, as well asgroups
andsubset
if appli - class
- a character string for the class of interest
- cuts
- the number of splits of the data are used to create the plot.
- subset
- An expression that evaluates to a logical or integer indexing vector. It is evaluated in
data
. Only the resulting rows ofdata
are used for the plot. - lattice.options
- A list that could be supplied to
lattice.options
- xlabel
- a text label for the x axis
- ylabel
- a text label for the y axis
- ...
- options to pass through to
xyplot
or the panel function
Details
To construct the lift plot, the following steps are used for each model:
- The data are ordered by the numeric model prediction used on the right-hand side of the model formula
- The data are split into
cuts - 1
roughly equal groups - the number of samples with true results equal to
class
are determined - the lift is calculated as the ratio of the percentage of samples in each split corresponding to
class
over the same percentage in the entire data set
lift
produces a plot of the cumulative lift values by the percentage of samples evaluated. This implementation uses the 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
).
Value
- a
lattice object
See Also
Examples
set.seed(1)
simulated <- data.frame(obs = factor(rep(letters[1:2], each = 100)),
perfect = sort(runif(200), decreasing = TRUE),
random = runif(200))
lift(obs ~ random, data = simulated)
lift(obs ~ random, data = simulated, type = c("p", "l"))
lift(obs ~ random + perfect, data = simulated,
type = c("p", "l"),
auto.key = list(columns = 2))
Community examples
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