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Rulex can solve many types of problems with a full range of tasks.

These types of problems can be divided into the following categories

problems

Problem Type

Description

Available tasks

Optimization

Optimization tasks solve problems related to optimizing a situation, given an objective function and a set of constraints.

Supervised

In supervised learning

 

 problems an output attribute is present in the dataset and the target of the analysis is to derive a model that describes the relationship between this output attribute and other input attributes in the dataset.

Unsupervised

In unsupervised learning

 problems

 problems no output variables are present, and examples included in the dataset are partitioned into a

number of

number of groups (clusters), according to a given measure of similarity: two examples belonging to the same group must exhibit a higher value of similarity than two patterns associated with different clusters.