CBA_ruleset: Constructor for Objects for Classifiers Based on Association...

In ianjjohnson/arulesCBA: Classification Based on Association Rules

Constructor for Objects for Classifiers Based on Association Rules

Description

Objects for classifiers based on association rules have class "CBA". A creator function CBA_ruleset() and several methods are provided.

Usage

CBA_ruleset(
  formula,
  rules,
  default = NA,
  method = "first",
  weights = NULL,
  bias = NULL,
  model = NULL,
  discretization = NULL,
  description = "Custom rule set",
  ...
)

rules(x)

## S3 method for class 'CBA'
rules(x)

## S3 method for class 'CBA'
predict(object, newdata, type = c("class", "score"), ...)

Arguments

formula	A symbolic description of the model to be fitted. Has to be of form class ~ .. The class is the variable name (part of the item label before =).
rules	A set of class association rules mined with mineCars or apriori (from arules).
default	Default class. If not specified then objects that are not matched by rules are classified as NA.
method	Classification method "first" found rule or "majority".
weights	Rule weights for method majority. Either a quality measure available in rules or a numeric vector of the same length are rules can be specified. If missing, then equal weights are used
bias	Class bias vector.
model	An optional list with model information (e.g., parameters).
discretization	A list with discretization information used by predict to discretize data supplied as a data.frame.
description	Description field used when the classifier is printed.
...	Additional arguments added as list elements to the CBA object.
x, object	An object of class CBA.
newdata	A data.frame or transactions containing rows of new entries to be classified.
type	Predict "class" labels. Some classifiers can also return "scores".

Details

CBA_ruleset creates a new object of class CBA using the provides rules as the rule base. For method "first", the user needs to make sure that the rules are predictive and sorted from most to least predictive.

Value

CBA_ruleset() returns an object of class CBA representing the trained classifier with fields:

formula	used formula.
rules	the classifier rule base.
default	default class label or NA.
method	classification method.
weights	rule weights.
bias	class bias vector if available.
model	list with model description.
discretization	discretization information.
description	description in human readable form.

predict returns predicted labels for newdata.

rules returns the rule base.

Author(s)

Michael Hahsler

See Also

CBA, mineCARs, apriori, rules, transactions.

Examples

data("iris")

# discretize and create transactions
iris.disc <- discretizeDF.supervised(Species ~., iris)
trans <- as(iris.disc, "transactions")

# create rule base with CARs
cars <- mineCARs(Species ~ ., trans, parameter = list(support = .01, confidence = .8))

cars <- cars[!is.redundant(cars)]
cars <- sort(cars, by = "conf")

# create classifier and use the majority class as the default if no rule matches.
cl <- CBA_ruleset(Species ~ ., cars, method = "first",
  default = uncoveredMajorityClass(Species ~ ., trans, cars))
cl

# look at the rule base
rules(cl)

# make predictions
prediction <- predict(cl, trans)
table(prediction, response(Species ~ ., trans))

# use weighted majority
cl <- CBA_ruleset(Species ~ ., cars, method = "majority", weights = "lift",
  default = uncoveredMajorityClass(Species ~ ., trans, cars))
cl

prediction <- predict(cl, trans)
table(prediction, response(Species ~ ., trans))

ianjjohnson/arulesCBA documentation built on June 13, 2022, 2:07 p.m.