![]() To use these zip files with Auto-WEKA, you need to pass them to an InstanceGenerator that will split them up into different subsets to allow for processes like cross-validation. Each zip has two files, test.arff and train.arff in WEKA's native format. ![]() We have been asked to provide the raw state files for the SMAC runs on these datasets (Note that they are not using the same version of Auto-WEKA as in the KDD paper). Below are some sample datasets that have been used with Auto-WEKA. See the manual provided with Auto-WEKA for more details on how to chain InstanceGenerators together. When running an Auto-WEKA wrapper, you can then use the following 10 lines as an instanceString: To perform 10 fold cross-validation with a specific seed, you can use the following line for your instanceGeneratorArgs that you pass to the ExperimentConstructor: ![]() ![]() The algorithms can either be applied directly to a dataset or called from your own Java code. It is written in Java and runs on almost any platform. Auto-WEKA : Sample Datasets Auto-WEKA : Sample Datasetsīelow are some sample datasets that have been used with Auto-WEKA. Weka is a collection of machine learning algorithms for solving real-world data mining problems.
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