C50Model | R Documentation |
Fit classification tree models or rule-based models using Quinlan's C5.0 algorithm.
C50Model( trials = 1, rules = FALSE, subset = TRUE, bands = 0, winnow = FALSE, noGlobalPruning = FALSE, CF = 0.25, minCases = 2, fuzzyThreshold = FALSE, sample = 0, earlyStopping = TRUE )
trials |
integer number of boosting iterations. |
rules |
logical indicating whether to decompose the tree into a rule-based model. |
subset |
logical indicating whether the model should evaluate groups of discrete predictors for splits. |
bands |
integer between 2 and 1000 specifying a number of bands into which to group rules ordered by their affect on the error rate. |
winnow |
logical indicating use of predictor winnowing (i.e. feature selection). |
noGlobalPruning |
logical indicating a final, global pruning step to simplify the tree. |
CF |
number in (0, 1) for the confidence factor. |
minCases |
integer for the smallest number of samples that must be put in at least two of the splits. |
fuzzyThreshold |
logical indicating whether to evaluate possible advanced splits of the data. |
sample |
value between (0, 0.999) that specifies the random proportion of data to use in training the model. |
earlyStopping |
logical indicating whether the internal method for stopping boosting should be used. |
factor
trials
, rules
, winnow
Latter arguments are passed to C5.0Control
.
Further model details can be found in the source link below.
In calls to varimp
for C50Model
, argument type
may be specified as "usage"
(default) for the percentage of training
set samples that fall into all terminal nodes after the split of each
predictor or as "splits"
for the percentage of splits associated with
each predictor. Variable importance is automatically scaled to range from 0
to 100. To obtain unscaled importance values, set scale = FALSE
. See
example below.
MLModel
class object.
C5.0
, fit
, resample
## Requires prior installation of suggested package C50 to run model_fit <- fit(Species ~ ., data = iris, model = C50Model) varimp(model_fit, method = "model", type = "splits", scale = FALSE)
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