RCAR {arulesCBA}  R Documentation 
Build a classifier based on association rules mined for an input dataset and weighted with LASSO regularized logistic regression following RCAR (Azmi, et al., 2019). RCAR+ extends RCAR from a binary classifier to a multiclass classifier and can use supportbalanced CARs.
RCAR(
formula,
data,
lambda = NULL,
alpha = 1,
glmnet.args = NULL,
cv.glmnet.args = NULL,
parameter = NULL,
control = NULL,
balanceSupport = FALSE,
disc.method = "mdlp",
verbose = FALSE,
...
)
formula 
A symbolic description of the model to be fitted. Has to be
of form 
data 
A data.frame or arules::transactions containing the training data.
Data frames are automatically discretized and converted to transactions with

lambda 
The amount of weight given to regularization during the
logistic regression learning process. If not specified ( 
alpha 
The elastic net mixing parameter. 
cv.glmnet.args , glmnet.args 
A list of arguments passed on to

parameter , control 
Optional parameter and control lists for 
balanceSupport 
balanceSupport parameter passed to 
disc.method 
Discretization method for factorizing numeric input
(default: 
verbose 
Report progress? 
... 
For convenience, additional parameters are used to create the

RCAR+ extends RCAR from a binary classifier to a multiclass classifier using regularized multinomial logistic regression via glmnet.
If lambda is not specified (NULL
) then crossvalidation with the
largest value of lambda such that error is within 1 standard error of the
minimum is used to determine the best value (see cv.glmnet()
also for how to
perform crossvalidation in parallel).
Returns an object of class CBA representing the trained
classifier with the additional field model
containing a list with the
following elements:
all_rules 
all rules used to build the classifier, including the rules with a weight of zero. 
reg_model 
them multinomial logistic
regression model as an object of class 
cv 
contains the results for the crossvalidation used determine lambda. 
Tyler Giallanza and Michael Hahsler
M. Azmi, G.C. Runger, and A. Berrado (2019). Interpretable regularized class association rules algorithm for classification in a categorical data space. Information Sciences, Volume 483, May 2019. Pages 313331.
data("iris")
classifier < RCAR(Species~., iris)
classifier
# inspect the rule base sorted by the larges class weight
inspect(sort(classifier$rules, by = "weight"))
# make predictions for the first few instances of iris
predict(classifier, head(iris))
# inspecting the regression model, plot the regularization path, and
# plot the crossvalidation results to determine lambda
str(classifier$model$reg_model)
plot(classifier$model$reg_model)
plot(classifier$model$cv)
# show progress report and use 5 instead of the default 10 crossvalidation folds.
classifier < RCAR(Species~., iris, cv.glmnet.args = list(nfolds = 5), verbose = TRUE)