mljar-supervised VS automlbenchmark

Compare mljar-supervised vs automlbenchmark and see what are their differences.

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mljar-supervised automlbenchmark
51 3
2,912 372
1.1% 2.2%
8.7 6.9
21 days ago 10 days ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

mljar-supervised

Posts with mentions or reviews of mljar-supervised. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-24.

automlbenchmark

Posts with mentions or reviews of automlbenchmark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-24.

What are some alternatives?

When comparing mljar-supervised and automlbenchmark you can also consider the following projects:

optuna - A hyperparameter optimization framework

autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data

autokeras - AutoML library for deep learning

LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

PySR - High-Performance Symbolic Regression in Python and Julia

mljar-examples - Examples how MLJAR can be used

studio - MLJAR Studio Desktop Application

Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

xgboost_ray - Distributed XGBoost on Ray