Solving Supervised Learning Problems
With Rulex, you can solve Supervised learning problems, where 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.
Depending on the output variable type, two different supervised learning problems can be defined:
classification problems aim to define which class or category input attributes in a dataset belong. The output attribute that defines the target class or category is a nominal attribute. For example, a classification problem may predict whether car sales will increase, decrease or remain stable (3 possible outcomes) over the next 12 months.
regression problems aim to predict a continuous quantity output for a set of input attributes in a dataset. For example a regression problem may predict how much a house will be worth in twelve months from now (there are many possible outcomes, all of which are continuous values).
Rulex's Logic Learning Machine
Rulex has its own proprietary algorithm for dealing with supervised learning problems, called the Logic Learning machine (or simply LLM).
The LLM algorithm produces intelligible rules, pretty much the same way as the human brain creates predictions, using learned logic.
For example:
IF weather = raining AND meeting before 9am = FALSE, THEN Best Method of Transport = Bus IF weather = sunny AND meeting before 9am = FALSE, THEN Best Method of Transport = Bicycle IF weather = raining AND meeting before 9am = TRUE, THEN Best Method of Transport = Car
These human readable and clear rules are expressed as if-then rules within Rulex, and allow users to understand and explain the reasoning behind predictions.
This makes Rulex the leader in eXplainable AI (XAI) and provides an ethical and GDPR compliant AI tool.
The following three LLM tasks are provided to solve classification, regression and one-class problems:
Problem | Description | Corresponding page |
|---|---|---|
Classification | The aim of classification problems is to define which class or category input attributes in a dataset belong. The output attribute that defines the target class or category is a nominal attribute. | |
Regression | The aim of regression problems is to predict a continuous quantity output for a set of input attributes in a dataset. | |
One-Class | One-class classification problems, where all the data under analysis belongs to the class, is mainly used to subsequently detect anomalies or outliers. |
Rulex Classification Tasks
Apart from its proprietary LLM Classification task, Rulex offers a full range of "traditional" machine learning tasks for solving classification problems.
Task | Description | Corresponding page |
|---|---|---|
Decision Tree | Solves classification problems by building a tree structure of intelligible rules. | |
K-Nearest Neighbor | Determines the output of a new example on the basis its nearest neighbors. | |
Logistic | Solves classification problems according to the logistic regression approach. | |
Neural Networks | Builds a feed-forward neural network for classification. | |
SVM | Trains a Support Vector Machine. |
Rulex Regression Tasks
Along with its proprietary LLM Regression task, Rulex offers a full range of "traditional" machine learning tasks for solving regression problems.
Task | Description | Corresponding page |
|---|---|---|
Auto Regressive | Predicts the future values assumed by a signal over time, principally based on its past behavior. | |
K-Nearest Neighbor | Determines the output of a new example on the basis its nearest neighbors. | |
Linear | Solves regression problems in which the output value is expected to be a linear combination of the input variables through the Ordinary Least Squares (OLS) method. | |
Neural Networks | Builds a feed-forward neural network for regression. | |
Regression SVM | Trains a Support Vector Machine for regression. | |
Regression Tree | Builds a regression model using the decision tree approach. |