Download nz.ac.waikato.cms.weka.thirdparty JAR files with all dependencies

External (non-maven based) Andromda dependency.

This plugin inserts implements interface, into the class header of generated classes, according to customization extensions. This lets you process XML content nodes not only by class but also by interface. This simplifies the handling of field subsets common to different schema types.

Knowledge Flow plugin that provides a Knowledge Flow step that wraps around a Groovy script. The plugin generates a fully compilable template Groovy script that implements various Knowledge Flow interfaces. The user can fill in the methods that are necessary to accomplish the desired logic. The script is compiled at runtime and the Groovy component passes incoming events to the script and collects and passes on generated events.

Allow you to write add some code into the generated code, and have XJC preserve them.

A plugin that adds parent pointers to classes generated by xjc.

ivy-s3-resolver

Java client for the Zendesk API

Grows an alternating model tree by minimising squared error. For more information see "Eibe Frank, Michael Mayo, Stefan Kramer: Alternating Model Trees. In: Proceedings of the ACM Symposium on Applied Computing, Data Mining Track, 2015".

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.

This package provides two classes - one for evaluating the merit of individual attributes using a classifier (ClassifierAttributeEval), and second for evaluating the merit of subsets of attributes using a classifier (ClassifierSubsetEval). Both invoke a user-specified classifier to perform the evaluation, either under cross-validation or on the training data.

An implementation of the elastic net method for linear regression