C50: plot.C5.0 – R documentation

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C50

plot.C5.0

Plot a decision tree

Description

Plot a decision tree.

Usage

## S3 method for class 'C5.0'
plot(x, trial = 0, subtree = NULL, ...)

Arguments

`x`	an object of class `C5.0`
`trial`	an integer for how many boosting iterations are used for prediction. NOTE: the internals of `C5.0` are zero-based so to get the initial decision tree you must use `trial = 0`. If `trial` is set too large, it is reset to the largest value and a warning is given.
`subtree`	an optional integer that can be used to isolate nodes below the specified split. See `partykit::party()` for more details.
`...`	options passed to `partykit::plot.party()`

Value

No value is returned; a plot is rendered.

Author(s)

Mark Culp, Max Kuhn

References

Quinlan R (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, http://www.rulequest.com/see5-unix.html

Examples

mod1 <- C5.0(Species ~ ., data = iris)
plot(mod1)
plot(mod1, subtree = 3)


mod2 <- C5.0(Species ~ ., data = iris, trials = 10)
plot(mod2) ## should be the same as above

## plot first weighted tree
plot(mod2, trial = 1)

C50

C5.0 Decision Trees and Rule-Based Models

v0.1.3.1

GPL-3

Authors

Max Kuhn [aut, cre], Steve Weston [ctb], Mark Culp [ctb], Nathan Coulter [ctb], Ross Quinlan [aut] (Author of imported C code), RuleQuest Research [cph] (Copyright holder of imported C code), Rulequest Research Pty Ltd. [cph] (Copyright holder of imported C code)

Initial release