C5.0.default {C50}  R Documentation 
C5.0 Decision Trees and RuleBased Models
Description
Fit classification tree models or rulebased models using Quinlan's C5.0 algorithm
Usage
## Default S3 method:
C5.0(
x,
y,
trials = 1,
rules = FALSE,
weights = NULL,
control = C5.0Control(),
costs = NULL,
...
)
## S3 method for class 'formula'
C5.0(formula, data, weights, subset, na.action = na.pass, ...)
Arguments
x 
a data frame or matrix of predictors. 
y 
a factor vector with 2 or more levels 
trials 
an integer specifying the number of boosting iterations. A value of one indicates that a single model is used. 
rules 
A logical: should the tree be decomposed into a rulebased model? 
weights 
an optional numeric vector of case weights. Note that the data used for the case weights will not be used as a splitting variable in the model (see http://www.rulequest.com/see5win.html#CASEWEIGHT for Quinlan's notes on case weights). 
control 
a list of control parameters; see

costs 
a matrix of costs associated with the possible errors. The matrix should have C columns and rows where C is the number of class levels. 
... 
other options to pass into the function (not currently used with default method) 
formula 
a formula, with a response and at least one predictor. 
data 
an optional data frame in which to interpret the variables named in the formula. 
subset 
optional expression saying that only a subset of the rows of the data should be used in the fit. 
na.action 
a function which indicates what should happen
when the data contain 
Details
This model extends the C4.5 classification algorithms described in Quinlan (1992). The details of the extensions are largely undocumented. The model can take the form of a full decision tree or a collection of rules (or boosted versions of either).
When using the formula method, factors and other classes are preserved (i.e. dummy variables are not automatically created). This particular model handles nonnumeric data of some types (such as character, factor and ordered data).
The cost matrix should by CxC, where C is the number of
classes. Diagonal elements are ignored. Columns should
correspond to the true classes and rows are the predicted
classes. For example, if C = 3 with classes Red, Blue and Green
(in that order), a value of 5 in the (2,3) element of the matrix
would indicate that the cost of predicting a Green sample as
Blue is five times the usual value (of one). Note that when
costs are used, class probabilities cannot be generated using
predict.C5.0()
.
Internally, the code will attempt to halt boosting if it
appears to be ineffective. For this reason, the value of
trials
may be different from what the model actually
produced. There is an option to turn this off in
C5.0Control()
.
Value
An object of class C5.0
with elements:
boostResults 
a parsed version of the boosting table(s) shown in the output 
call 
the function call 
caseWeights 
not currently supported. 
control 
an echo of the specifications from

cost 
the text version of the cost matrix (or "") 
costMatrix 
an echo of the model argument 
dims 
original dimensions of the predictor matrix or data frame 
levels 
a character vector of factor levels for the outcome 
names 
a string version of the names file 
output 
a string version of the command line output 
predictors 
a character vector of predictor names 
rbm 
a logical for rules 
rules 
a character version of the rules file 
size 
n integer vector of the tree/rule size (or sizes in the case of boosting) 
.
tree 
a string version of the tree file 
trials 
a named vector with elements 
Note
The command line version currently supports more data types than the R port. Currently, numeric, factor and ordered factors are allowed as predictors.
Author(s)
Original GPL C code by Ross Quinlan, R code and modifications to C by Max Kuhn, Steve Weston and Nathan Coulter
References
Quinlan R (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, http://www.rulequest.com/see5unix.html
See Also
C5.0Control()
, summary.C5.0()
,
predict.C5.0()
, C5imp()
Examples
library(modeldata)
data(mlc_churn)
treeModel < C5.0(x = mlc_churn[1:3333, 20], y = mlc_churn$churn[1:3333])
treeModel
summary(treeModel)
ruleModel < C5.0(churn ~ ., data = mlc_churn[1:3333, ], rules = TRUE)
ruleModel
summary(ruleModel)