FLAML vs Weka

FLAML is free, MIT-licensed open-source software for automated machine learning and tuning. It seeks accurate models or configurations while using low computational resources, and can adapt large language models to applications to reduce monetary costs. Supported AutoML tasks include classification, regression, time-series and panel forecasting, learning to rank, and sequence classification. The flaml.default package recommends data-dependent hyperparameter defaults without runtime tuning. Tuning handles large search spaces, different evaluation costs, constraints, guidance, and early stopping; users can restrict learners or tune a customized learner. Optional integrations include OpenAI models, CatBoost, Vowpal Wabbit, Prophet, statsmodels, Hugging Face Transformers, Ray, Spark, NNI, BlendSearch, Azure Synapse, MLflow, and Azure Machine Learning. Python support for the latest version is 3.10 or later and earlier than 3.14. Ray and Spark enable parallel tuning, but not together, and Spark parallel training does not support GPUs. A .NET implementation is available through ML.NET.

Weka is open-source machine-learning software for building and evaluating models, with support for forecasting and time-series modeling. It is available free under the GNU General Public License, with Weka 3.8 as the stable release and 3.9 as the development version. The stable branch receives fixes and feature updates intended to preserve compatibility; development builds may introduce breaking changes. Weka provides a Java API, Javadoc for its API and command-line parameters, and can also be used through Jupyter notebooks. Official releases require Java 8 or later. Downloads are listed for Windows, Mac OS, and Linux, with Intel and ARM options. Both branches include a package manager for community extensions, though installing packages requires internet access. The software includes built-in help and a comprehensive manual, with additional assistance through documentation, mailing list archives, forums, and a mailing list. Distributed derivative works must use the GPL; commercial projects needing Weka code in a non-GPL program may be able to obtain a suitable license. Models serialized in version 3.7 are incompatible with 3.8, and the migrator has an exception for RandomForest.