YAAML
Lightweight, production-ready AutoML built entirely on scikit-learn with Python 3.12+ modernization
YAAML (Yet Another AutoML) is an old attempt at creating a fully-automated machine learning package. It is built entirely on scikit-learn without complex dependencies, focusing on modernization and zero-bloat.
Overview
Designed to sit directly on top of sklearn, YAAML aims to streamline the modeling workflow through an intelligent YAAMLAutoML wrapper pipeline.
Key Capabilities
- Dependency-Free Backbone: No massive deep learning libraries required—runs natively with scikit-learn.
- Smart Data Preprocessing: Handles missing data imputation, target encoding, and multi-type dataframes.
- Multi-Task Support: Automatically detects and adapts execution for classification vs regression.
- Modern Python Validation: Fully typed with Python 3.12+ features, using union types and the walrus operator.
Architecture
The system treats feature engineering and model selection as a unified Search Space, utilizing hyperopt or randomized search internally to discover the best sequence of transformations and estimator configurations.
This provides a true end-to-end framework where categorical casting, target encoding, data scaling, and model hyperparameters are optimized holistically.
Source
Available on GitHub and built to handle complex, messy real-world datasets in a transparent sklearn pipeline object.