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Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
The main features of mljar/mljar-supervised are: Automated Machine Learning, AutoML Frameworks, Explainability and Fairness, General Machine Learning.
Projects with overlapping indexed features include: epistasislab/tpot — TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes… 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,… automl/auto-sklearn — This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters.… hips/spearmint — Spearmint Bayesian optimization codebase. interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training…
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.
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
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
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.