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Automated modeling and machine learning framework FEDOT
The main features of nccr-itmo/fedot are: Automated Machine Learning, General Machine Learning.
Open-source alternatives to nccr-itmo/fedot include: mljar/mljar-supervised — Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and… epistasislab/tpot — TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes… hips/spearmint — Spearmint Bayesian optimization codebase. awslabs/autogluon — AutoGluon is an automated machine learning framework designed to optimize model selection and hyperparameter tuning… determined-ai/determined — Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning,… optuna/optuna — Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine…
TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes machine learning algorithms and hyperparameters to identify the most effective model architecture. The system utilizes genetic programming to optimize these pipelines through evolutionary algorithms. To accelerate the search process, it functions as a multi-core evaluator that runs parallel training workflows across multiple processor cores. The framework supports the definition of custom objective functions to optimize pipelines based on specific performance metrics.
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
AutoGluon is an automated machine learning framework designed to optimize model selection and hyperparameter tuning across tabular, text, image, and time series data. It functions as an ensemble learning library and a tabular data prediction engine, aiming to build high-accuracy predictive models without manual algorithm selection. The framework integrates multimodal machine learning pipelines that combine disparate data types into a single representation using specialized encoders. It also includes a probabilistic time series forecaster that fits multiple statistical and deep learning models
