A metaclassifier that makes its base classifier cost-sensitive
Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class).Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
- Type: Data Training data
- Type: Weka 3.7 Classifier Trained model
Analytics > Mining > Weka > Weka (3.7) > Classification Algorithms > meta
Make sure to have this extension installed: