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EvalML is an AutoML library written in python.
The main features of alteryx/evalml are: Automated Machine Learning, AutoML.
Projects with overlapping indexed features include: microsoft/flaml — FLAML is an automated machine learning framework, hyperparameter optimization tool, and large language model agent… automl/auto-sklearn — This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters.… keras-team/autokeras — AutoKeras is an automated machine learning framework and Keras AutoML library designed to discover the most effective… pycaret/pycaret — PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It… autogluon/autogluon — AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end… axelderomblay/mlbox — MLBox is a powerful Automated Machine Learning python library.
AutoKeras is an automated machine learning framework and Keras AutoML library designed to discover the most effective deep learning model structures for a given dataset. It functions as a tool for deep learning architecture search, eliminating manual hyperparameter tuning by automatically searching for and optimizing neural network architectures. The framework provides capabilities for benchmarking and refining neural network designs to maximize performance. It includes a system for containerized machine learning deployment, allowing environments to be packaged into containers to ensure consi
FLAML is an automated machine learning framework, hyperparameter optimization tool, and large language model agent orchestrator. It provides a system for model selection and tuning across various learners and datasets, while also offering a toolkit for optimizing the inference parameters and fine-tuning settings of large language models. The project features a meta-learning tuning system that analyzes historical task data to generate data-dependent default configurations, accelerating model convergence. It further enables the design of collaborative multi-agent systems through conversational
This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur
AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc