MenuBL

MenuBL is a software framework designed to streamline machine learning experimentation through a menu-driven interface. The name reflects its core interaction model, guiding users through steps such as data selection, model choice, preprocessing, and evaluation without requiring extensive scripting. By emphasizing guided configuration, menuBL aims to make experimentation more approachable while preserving flexibility for advanced users.

The architecture of menuBL centers on modularity and extensibility. It provides a command-line interface and, in some deployments, an optional graphical user interface. Key components include a plugin-based model registry, dataset adapters, and an experiment ledger that records configurations, parameters, and results. The design prioritizes reproducibility, with exportable configuration profiles, deterministic execution when random seeds are fixed, and support for versioned experiments.

Core features include templates for common tasks (classification, regression, clustering), support for multiple backends through adapters (such as scikit-learn, PyTorch, or TensorFlow), and facilities for cross-validation, hyperparameter sweeps, and benchmarking. Visualization helpers offer learning curves and performance charts to aid analysis. The system is designed to be cross-platform and adaptable to different research or educational contexts.

Typical use cases involve rapid prototyping and evaluation in research settings or classroom environments. By removing much of the boilerplate scripting, menuBL helps users compare models, document workflows, and share reproducible experiments. While primarily aimed at education and experimentation, the framework supports extensions and integrations that enable more complex pipelines and bespoke analyses.

MenuBL is an open-source project maintained by a community of contributors, with ongoing updates and documentation to reflect evolving practices in machine learning experimentation.