All Classes and Interfaces
Class
Description
Abstract base class for growing and pruning procedures for all types of rules (classification, regression, survival).
Abstract base class for all separate and conquer algorithms for induction of rule-based models
(classification, regression, survival).
Auxilliary class that compares attributes w.r.t.
Class gathering additional performance measures for balanced accuracy.
This class encapsulates the well known binary classification criteria precision and recall.
Class representing Chi-square test for variance.
Class for growing and pruning classification rules with user's knowledge.
User-guided separate'n'conquer algorithm for generating classification rule sets.
Class for growing and pruning classification rules.
Class gathering all quality measures for classification problems.
Class representing a classification rule.
Class representing a set of classification rules.
Class gathering additional performance measures for classification models (avg.
Separate'n'conquer algorithm for generating classification rule sets.
Class representing a compound condition.
Abstract base class representing all conditions.
Represents condition type:
Helper class for storing information about evaluated condition.
Class representing contingency table.
Computes the empirical corelation coefficient 'r' between label and prediction.
Represents covering of a rule.
Represents an elementary condition (built upon single attribute and value set).
Console used in batch mode.
An ExpertRuleGenerator is an operator that extends RuleGenerator by providing user
with the possibility to introduce users's knowledge to the rule induction process.
Class representing a hypergeometric statistical test.
Interface to be implemented by all classes representing user-guided rule induction procedures.
Class representing all parameters of rule induction algorithm.
Upper-bounded set of integers represented internally as a bit vector.
Class representing integrated Brioer score - a performance measures for survival models.
Represents continuous interval.
Interface to be implemented by all classes representing quality measures.
Interface to be implemented by user-defined measures.
Inteface to be implemented by all classes representing set of values (discrete set, interval, sum of sets, etc.).
Represents a Kaplan-Meier estimator of survival function.
Class representing user's knowledge.
Auxiliary singleton class for logging.
Enum representing possible logical operators in a rule premise - conjuction and alternative.
Class representing log-rank test.
Class defining missing value handling method.
Measures the accuracy and classification error for both binary classification problems and multi
class problems.
Generic class representing a multiset.
Class gathering number of negative voting conflicts).
Normalized absolute error is the total absolute error normalized by the error simply predicting
the average of the actual values.
Class for growing and pruning regression rules with user's knowledge.
User-guided separate'n'conquer algorithm for generating regression rule sets.
Algorithm for growing and pruning regression rules.
Class representing regression rule.
Class representing a set of regression rules.
Separate'n'conquer algorithm for generating regression rule sets.
Relative squared error is the total squared error made relative to what the error would have been
if the prediction had been the average of the absolute value.
Abstract class representing all kinds of rules (classification/regression/survival).
A factory class for creating instances of rules and rule sets of different types (classification/regression,/survival).
The basic RuleKit learner operator.
Auxiliary enumeration type describing possible destinations of quality measures.
Class for parsing rules from text.
Abstract class representing all rule-based models (classification/regression/survival).
Class used to run test process to profile execution of prediction
Auxiliary class for handling sets.
Simple criteria are those which error can be counted for each example and can be averaged by the
number of examples.
Value set containing one element.
Computes the square of the empirical corellation coefficient 'r' between label and prediction.
The basic RuleKit learner operator.
Abstract base class for all statistical tests.
Class for growing and pruning log rank-based survival rules with user's knowledge.
Separate'n'conquer algorithm for generating log rank-based survival rule sets with user's knowledge.
Class for growing and pruning log rank-based survival rules.
Separate'n'conquer algorithm for generating log rank-based survival rule sets.
Class representing a survival rule.
Class representing a set of survival rules.