AutoML

Auto_ML is a Python-based library designed to automate the whole machine learning process. It focuses on simplifying the model selection, feature engineering, hyperparameter tuning, data formatting, robust scaling and analytics. It supports binary and multiclass classification, regression, linear-model-esque interpretation from non-linear models, feature learning, and categorical ensembling. The package includes traditional models, as well as deep learning models, gradient boost models, and catboost models.

Link: https://pypi.org/project/auto_ml/

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.

(1) AutoGluon-Tabular is an AutoML framework for tabular data. It succeeds by ensembling multiple models and stacking them in multiple layers.
(2) AutoGluon-MultiModal is a deep learning model zoo of model zoos that can automatically build state-of-the-art deep learning models for inputs including images, text, and tabular data.
(3) AutoGluon-TimeSeries is designed for probabilistic time series forecasting. It combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques.

Link: https://auto.gluon.ai/stable/index.html
Youtube Link: https://www.youtube.com/watch?v=5tvp_Ihgnuk

Auto-Keras is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. Auto-Keras uses building blocks to quickly construct personalized models. With these blocks, users only need to specify the high-level architecture of the model. AutoKeras would search for the best detailed configuration, or users can override the base classes to create their own block.

Link: https://autokeras.com/

Auto-PyTorch is able to jointly and robustly optimize the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series data (forecasting).

Link: https://automl.github.io/Auto-PyTorch/master/

Auto-sklearn provides out-of-the-box supervised machine learning. Built around the scikit-learn machine learning library, auto-sklearn automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its hyperparameters. Thus, it frees the machine learning practitioner from these tedious tasks and allows her to focus on the real problem.

Link: https://www.automl.org/automl-for-x/tabular-data/auto-sklearn/

Auto-WEKA is a Java-written machine learning automation tool that performs combined algorithm selection and hyperparameter optimization over the classification and regression algorithm implementations in WEKA, an open-source software package including a comprehensive collection of machine learning models. It applies techniques including meta-learning and Bayesian optimization to explore optimal hyperparameters. With the automated process, Auto-WEKA provides time-saving model selection.

Link: https://www.cs.ubc.ca/labs/algorithms/Projects/autoweka/#

FEDOT is an open-source framework for automated modeling and machine learning (AutoML) problems. This framework is distributed under the 3-Clause BSD license. It provides automatic generative design of machine learning pipelines for various real-world problems. The core of FEDOT is based on an evolutionary approach and supports classification (binary and multiclass), regression, clustering, and time series prediction problems.

Link: https://fedot.readthedocs.io/en/latest/

FLAML is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, and optimizes their performance.

Link: https://microsoft.github.io/FLAML/

GAMA is an AutoML package for end-users and AutoML researchers. It generates optimized machine learning pipelines given specific input data and resource constraints. A machine learning pipeline contains data preprocessing (e.g. PCA, normalization) as well as a machine learning algorithm (e.g. Logistic Regression, Random Forests), with fine-tuned hyperparameter settings (e.g. number of trees in a Random Forest). To find these pipelines, multiple search procedures have been implemented. GAMA can also combine multiple tuned machine learning pipelines together into an ensemble, which on average should help model performance. At the moment, GAMA is restricted to classification and regression problems on tabular data. In addition to its general use AutoML functionality, GAMA aims to serve AutoML researchers as well. During the optimization process, GAMA keeps an extensive log of progress made. Using this log, insight can be obtained on the behavior of the search procedure.

Link: https://openml-labs.github.io/gama/master/

H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Flow notebook/web interface, and works seamlessly with big data technologies like Hadoop and Spark. H2O provides implementations of many popular algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks, Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Link: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html