Rule.evaluate

The rule we want to evaluate (Class "Rule").

rule

The keel dataset object with the examples to compare with the rule (Class "keel")

dataset

Matrix with the data of the dataset, one colum per rule. The data must not contain the last column, the class. (use .separar for this task and convert the list into a matrix)

data

a logical vector indicating which attributes in the dataset are categorical

categoricalValues

a logical vector indicating which attributes in the dataset are numerical

numericalValues

The T-norm to use. 0 for minimum t-norm, 1 for product t-norm (default: 1)

t_norm

An integer with the rule weighting method. <ul>
<li> 0 -> Classic Certainty Factor weight
</li>
<li> 1 -> Penalized Certainty Factor weight II
</li>
<li> 2 > Penalized Certainty Factor weight IV
</li>
<li> 3 -> No Rule Weight
</li>
</ul>


ruleWeight

Implementation of some algorithms for the data mining task called
"subgroup discovery" without package dependencies. It also provide a Shiny App
for make the analysis easier. The algorithms works with data sets provided in
KEEL or ARFF format files or data.frame objects. If you want more information about KEEL format, please
refer to < http://www.keel.es > .

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Rule.evaluate: Evaluate a single rule.

Description

Usage

Arguments

Value