mljar-supervised
automlbenchmark
| mljar-supervised | automlbenchmark | |
|---|---|---|
| 56 | 3 | |
| 3,266 | 460 | |
| 0.5% | 0.2% | |
| 7.0 | 1.0 | |
| 4 days ago | 7 days ago | |
| Python | Python | |
| MIT License | MIT License |
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
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Ask HN: What Are You Working On? (May 2026)
Im working on AI data analyst - MLJAR Studio. It is conversational UI with AI agent which uses Python to provide data insights. It is available as desktop application https://mljar.com
- Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks
- Python, notebooks, no code recipes, AI = new desktop app for data analysis
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Show HN: Supertree – interactive visualization of decision trees in Python
We would like to keep package sustainable. Earlier, we've created package for AutoML which is MIT license (https://github.com/mljar/mljar-supervised), and it is very hard to monetise it, and you need to have funds to keep package maintained and work on it.
Regarding purchasing, we just don't have time create landing page with buy button :) we will add it soon. The package cost will be 499 USD/yearly. We already have few finance companies interested.
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We need visual programming. No, not like that
I'm working on visual programming for Python. I created an Python editor, that is notebook based (similar to Jupyter) but each cell code in the notebook has graphical user interface. In this GUI you can select your code recipe, a simple code step, for example here is a recipe to list files in the directory https://mljar.com/docs/python-list-files-in-directory/ - you fill the UI and the code is generated. You can execute code cells in the top to bottom manner. In this approach you can click Python code. If you can't find UI with recipe, then you can ask AI assistant (running Llama3 with Ollama) or write custom python code. The app is called MLJAR Studio and it is a desktop based application, so all computations are running on your machine. You can read more on my website https://mljar.com
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Show HN: Web App with GUI for AutoML on Tabular Data
Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
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Fairness in machine learning
It's an Automated Machine Learning python package. It's open-source, you can see how it works on GitHub: https://github.com/mljar/mljar-supervised
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[P] Build data web apps in Jupyter Notebook with Python only
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- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
automlbenchmark
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Show HN: Web App with GUI for AutoML on Tabular Data
Here is benchmark done by independent team of researchers https://openml.github.io/automlbenchmark/
I think most of overfitting is avoided with early stoppoing technique.
The underfitting can be avoidwd with using large training time.
- Show HN: AutoAI
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Show HN: Mljar Automated Machine Learning for Tabular Data (Explanation,AutoDoc)
I'm also curious how does it compare! The package will be included in the newest comparison done by OpenML people https://github.com/openml/automlbenchmark
I have some old comparison of closed-source old system
What are some alternatives?
mercury - The fastest way to turn a Jupyter notebook into a beautiful, shareable web app — no callbacks, no frontend, no rewrite.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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.
autokeras - AutoML library for deep learning
autogluon - Fast and Accurate ML in 3 Lines of Code