CBA_helpers | R Documentation |
Helper functions to extract the response from transactions or rules, determine the class frequency, majority class, transaction coverage and the uncovered examples per class.
response(formula, x) classFrequency(formula, x, type = "relative") majorityClass(formula, transactions) transactionCoverage(transactions, rules) uncoveredClassExamples(formula, transactions, rules) uncoveredMajorityClass(formula, transactions, rules)
formula |
A symbolic description of the model to be fitted. |
x, transactions |
An object of class |
type |
|
rules |
A set of |
response
returns the response label as a factor.
classFrequency
returns the item frequency for each class label as a
vector.
majorityClass
returns the most frequent class label in the
transactions.
Michael Hahsler
itemFrequency
, rules
,
transactions
.
data("iris") iris.disc <- discretizeDF.supervised(Species ~ ., iris) iris.trans <- as(iris.disc, "transactions") inspect(head(iris.trans, n = 2)) # convert the class items back to a class label response(Species ~ ., head(iris.trans, n = 2)) # Class distribution. The iris dataset is perfectly balanced. classFrequency(Species ~ ., iris.trans) # Majority Class # (Note: since all class frequencies for iris are the same, the first one is returned) majorityClass(Species ~ ., iris.trans) # Use for CARs cars <- mineCARs(Species ~ ., iris.trans, parameter = list(support = 0.3)) # Number of rules for each class classFrequency(Species ~ ., cars, type = "absolute") # conclusion (item in the RHS) of the rule as a class label response(Species ~ ., cars) # How many rules (using the first three rules) cover each transactions? transactionCoverage(iris.trans, cars[1:3]) # Number of transactions per class not covered by the first three rules uncoveredClassExamples(Species ~ ., iris.trans, cars[1:3]) # Majority class of the uncovered examples uncoveredMajorityClass(Species ~ ., iris.trans, cars[1:3])
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