Search

Analyzing and Optimizing Results

When you have obtained your first results in Rulex, you will probably want to analyze and optimize them.

There are may tasks that allow you to do just this on the many structures produced by Rulex tasks.

For further information

Structures can also be converted into datasets and vice-versa to perform in-depth analysis operations.

For more information on this see the section Converting Structures

Available tasks

Task Name	Structure	Description	Corresponding Page
Rule Manager	Rules	Inspects, manipulates and optimizes sets of rules. Input Tasks This task can be used with any of the following LLM tasks that generate rulesets: Classification LLM Regression LLM One-Class LLM	Analyzing Rules in the Rule Manager Adding Rules Manually in the Rule Manager
Rule Viewer	Rules	Graphically displays rules, and retrieves information such as their importance, the relationship between classes of output attributes with respect to input attributes. Input Tasks This task can only be used with rules generated by the Classification LLM task.	Analyzing Classification LLM Rules in the Rule Viewer
Feature Ranking	Rules	Graphically displays the importance of attributes within a class values within specific attributes. Input tasks The task can be used only with rules that originate from one of the following tasks: Classification LLM Regression LLM	Ranking Rule Features and Values
Merge Rules	Rules	Merges rules from multiple computations.	Merging Rules
Optimize Ruleset	Rules	Improves the generation of predictive rules through a series of constraints.	Optimizing Rulesets
Association Manager	Association rules Replacement rules	Analyzes association and replacement rules, using filtering or sorting operations, computing statistics or creating plots. The task is very similar to the Data Manager, but is designed specifically for replacement and association rules.	Optimizing Association and Replacement Rules
Find/Replace	Rules	Replaces values that can be modified with new values to improve the outcome of the analysis.	Finding and Replacing Values in Datasets
Itemsets/Sequences Manager	Frequent Itemsets Frequent Sequences	Analyzes, filters and sorts frequent itemsets and sequences in a similar way to the Data manager task. Task input You must have produced clusters, itemsets or sequences in your process via one of the following tasks: Frequent Itemsets Mining Sequence Analysis	Analyzing Frequent Itemsets and Sequences
Confusion Matrix	Model	Computes and visualizes the performance of any classification method. Input tasks The task can be used with any classification task, but must be proceeded by an Apply Model task that tests the classification results on data.	Analyzing Model Performance in the Confusion Matrix
Apply Model	Model	Applies a model generated in Rulex on data. Input tasks Models can be made up of classification and regression rules and clusters, and the task can consequently be used after any classification, regression or clustering task.	Applying Models to Data