The process shows, using the Spambase dataset, how the quality, the precision of a given classification that is created based on a regression model fitted to a given data set can be evaluated. After the regression model has been built based on the training set, and the test set has been classified using it, the quality of the classification executed can be examined. Using the evaluation received this way, it can be decided whether the resulting classification is appropriate for the goals of the process, the existing model should be improved further, or the existing model is of such poor quality that using a completely new model is necessary.
Spambase [UCI MLR]
After creating the regression model, in order to be able to use it for classification, it has to be placed into an operator that implements regression-based classification. Similarly to when using the operator individually, it can be defined for example which method should be used for attribute selection, or what the level of minimal tolerance should be. The thus created linear regression model can be applied to the test set.
The following regression model is created based on the data of the training set:
Using the regression model created based on the records of the training set on the test set, confidence values can be calculated regarding the probabilities of the individual test records belonging to the given groups. Based on these confidence values, class assignments are assigned to the individual records of the test set. Corresponding to this, it can be evaluated how many records have been classified successfully based on the regression model: