AutoKeras vs LightAutoML

AutoKeras is a free, open-source AutoML system based on Keras, designed to make machine learning accessible through code. It supports image and text classification and regression, as well as classification and regression for structured data. Its documentation also includes a tutorial for multi-task or multimodal datasets. Users can assemble custom models from AutoKeras blocks and let the system search for detailed configurations. Image workflows can set architecture type, normalization and data augmentation, or leave those choices for automatic tuning. The best model found can be exported as a Keras Model. The Python package is installed with pip and supports Linux, macOS and Windows, including self-hosted use. The installation guide supports Python 3 and recommends PyTorch version 2.3.0 or later with the PyTorch backend. By default, the final 20% of training data is used for validation, though users can set a different split or supply validation data. Model selection uses validation loss by default, with support for customized metrics and objectives. The project directs users to GitHub Discussions for questions.

LightAutoML is an open-source Python library for automated machine learning on tabular and text data. It supports binary and multiclass classification and regression, using ready-made tabular, text and WhiteBox presets or modular custom pipelines. Pipeline creation can automate data processing and typing, feature selection, hyperparameter tuning, time utilization and report creation. Documented model classes include linear models, LightGBM and CatBoost boosted trees, neural networks and a WhiteBox scorecard. Its basic tutorial says the library can handle missing values and outliers automatically. Optional installation extras cover NLP, computer vision and reports, and a tutorial demonstrates a SQL data source. Installation is through PyPI with `pip install lightautoml`; the project is licensed under Apache License, Version 2.0. The maker describes it as a way for data scientists and analysts to reduce routine data preparation and model selection work. The current package handles datasets with independent samples in each row; multitable datasets and sequences remain in progress.