Then, follow userguide.pdf (please, note that PDF is from an older version. Auto-WEKA as a blackboxĪuto-WEKA includes a wizard to find the best MCPS of a given dataset without any user interaction, apart from providing a dataset and a time budget.Īuto-WEKA allows to select between a list of all WEKA methods and different optimisation strategies and parameters.Ĭlone the repository and run java -jar autoweka.jar. Our hope is that Auto-WEKA will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance. Auto-WEKA does this using a fully automated approach, leveraging recent innovations in Bayesian optimization. Auto-WEKA considers the problem of simultaneously composing an MCPS and setting its hyperparameters, going beyond previous methods that address these issues in isolation. However, each of these algorithms have their own hyperparameters that can drastically change their performance, and there are a staggeringly large number of possible alternatives overall. Many different machine learning algorithms exist that can easily be used off the shelf, many of these methods are implemented in the open source WEKA package.

This repository contains an extended version of Auto-WEKA now supporting the optimisation of MultiComponent Predictive Systems (MCPS). Auto-WEKA is a tool for automating the selection of methods and hyperparameters of WEKA.

Weka jar usage