rcar: Regularized class-association rules

In azemi/RCAR: Lasso Regularized CLASS-ASSOCIATION RULES BASED CLASSIFIER

Description

Fit a generalized linear model in the rules space via maximum likelihood with Lasso penalty. The regularization is computed over a grid of values or at a specifyed value for the regularization parameter lambda. It handle only categorical data space.

Usage

rcar(data, y.rank = 1, s = 0.3, c = 0.7, maxl = 6, lambd)

Arguments

data:	A categorical data set with binary response variable; each row is an observation vector.
y.rank:	The rank of the response variable.
s:	A user supplied value for the minimal support of an item set.
c:	A user supplied value for the minimal confidence of class-association rules.
maxl:	A user specified value for the maximal number of items per item set (default: 6 items).
lambd:	A user supplied value, or sequence of values, of the penalty terme. If it is not provided, the programme generate automatically a sequence ranging from the value of lambda such that all the coefficients are zero down to value of lambda whose the deviance do not change from lambda to the next.

Value

The function fits Lasso regularized logistic model with binary response on the rule space.

Examples

data(vote)

rcar.fit<-rcar(vote,y.rank=1, s=0.2, c=0.8)

100 RCAR models are fit on the rule space given by the 5084 rules mined using Apriori with minimum support and
confidence thresholds of (20%, 80%)


inspect(head(rcar.fit$Rules))

The first 6 rules are displayed, from left to right are shown for each CAR: the condition (lhs), the consequent(rhs),
the support, the confidence, and the frequence of the records that apply to the CAR.


rcar.fit

It shows from left to right the number of nonzero coefficients (Df), the percent (of null) deviance
explained (dev) and the value of <ce><bb> (Lambda).

plot(rcar.fit$Model,xvar = "lambda", label = TRUE)

displays the coefficients of the models fit on Congressional Voting data set using RCAR algorithm over a grids
of values of lambda. Each curve corresponds to a coefficient of a rule, it shows the variation of
coefficient against lambda. The number of retained rules in the model at the current lambda is indicated at
the axis above.

azemi/RCAR documentation built on May 7, 2019, 2:54 a.m.