Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Relative absolute error and relative root squared error are the mean absolute error and root mean squared error, divided by the corresponding error of the ZeroR classifier on the data (i.e. the classifier predicting the prior probabilities of the classes observed in the data).

Source: Weka FAQ

 Related Tips

• I have written a module. Can I sell it?
• 2D texture font - NeHe Tutorial JOGL Port
• 3D Human Animation Applet
• 3D lens flare with occlusion testing - NeHe Tutorial JOGL Port
• 3D shapes - NeHe Tutorial JOGL Port
• A model with a light which is moved by a modeling transformation
• A Morph object to animate a shape between two key shapes
• A red sphere using the Sphere utility class