D.3: Auto-Weka: Difference between revisions
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• '''Short Description''': | • '''Short Description''': | ||
Tool that performs combined algorithm selection and hyperparameter optimization over the classification and regression algorithms implements in WEKA. Auto-WEKA explores hyperparameter settings for a number of algorithms and recommends to a user which method will likely have good generalization performance, using model-based optimization techniques. | |||
• '''Reference, URL''': | • '''Reference, URL''': https://www.cs.ubc.ca/labs/algorithms/Projects/autoweka/ | ||
• ''' | • '''Applicable Business Category''': All/Smart Industry | ||
• '''Application in relevant Projects/Initiatives''': | • '''Application in relevant Projects/Initiatives''': | ||
• '''Type''': | • ''' Asset Type''': Executable, Platform | ||
• '''AI Breadth''': | • '''AI Breadth''': ML | ||
• '''Learning Ability''': | • '''Learning Ability''': Supervised & Unsupervised learning | ||
• '''Related technologies''': | • '''Related technologies''': Automated Machine Learning (AutoML), WEKA, Bayesian Optimisation | ||
• ''' | • '''Applicable Research Area''': Verifiable AI | ||
• ''' | • '''Applicable Technical Category''': Machine Learning | ||
• '''Audience''': | • '''License Information''': GNU General Public License version 3 | ||
• '''Related to circularity and sustainability''': Not directly | |||
• '''Audience''': Developers | |||
Latest revision as of 10:43, 27 December 2024
• Short Description:
Tool that performs combined algorithm selection and hyperparameter optimization over the classification and regression algorithms implements in WEKA. Auto-WEKA explores hyperparameter settings for a number of algorithms and recommends to a user which method will likely have good generalization performance, using model-based optimization techniques.
• Reference, URL: https://www.cs.ubc.ca/labs/algorithms/Projects/autoweka/
• Applicable Business Category: All/Smart Industry
• Application in relevant Projects/Initiatives:
• Asset Type: Executable, Platform
• AI Breadth: ML
• Learning Ability: Supervised & Unsupervised learning
• Related technologies: Automated Machine Learning (AutoML), WEKA, Bayesian Optimisation
• Applicable Research Area: Verifiable AI
• Applicable Technical Category: Machine Learning
• License Information: GNU General Public License version 3
• Related to circularity and sustainability: Not directly
• Audience: Developers
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