R: Plot a decision tree

plot.C5.0 {C50} | R Documentation

## 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

### See Also

`C5.0()`, `partykit::party()`

### 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)

[Package C50 version 0.1.5 Index]