Run CORELS entirely from your browser!
Here, you can upload a file containing any training dataset with all-binary features and a single binary classification, and then press 'Submit' to have CORELS generate a rule list model from it. To do this, CORELS will generate rule antecedents from the features of your dataset - first one antecedent will be generated for each feature, and then further antecedents will be generated by combining permutations of features. For example, if the dataset contains the features
is age > 25 and
is female, then this rule list mining would produce three rule antecedents:
is age > 25;
is female; and
is age > 25 AND is female.
In the above example, the third rule antecedent has a 'cardinality' of 2. For a dataset of m features, CORELS can generate, or mine, antecedents with cardinalities 1 through m, combining any number from 1 to all the features. However, limiting the maximum cardinality of these generated antecedents is often prudent, since for even a small numbers of features the number of total rules possible with high cardinalities is enormous. We provide an option to do this - CORELS will generate antecedents with cardinalities less than or equal to the value provided in the 'maximum cardinality' field.
We also provide an option to throw out antecedents that capture too many or too few samples - any antecedent that captures a fraction of the training samples that is less than or equal to the minimum support or that captures a fraction of the training samples that is greater than or equal to 1 minus the minimum support is discarded. Therefore the minimum support can have a value between 0 and 0.5.
For specific information about the data file formatting:CSV data format
Here are three example csv files containing datasets. Simply download one, upload it to the 'CSV data' panel below, and press 'Submit' to see CORELS in action. Or, you can run CORELS with a default dataset (COMPAS) without having to download anything by clicking on the link below the file upload field.COMPAS Monks1 Haberman