mljar-supervised
automlbenchmark
Our great sponsors
mljar-supervised | automlbenchmark | |
---|---|---|
51 | 3 | |
2,929 | 379 | |
1.2% | 3.2% | |
8.5 | 6.9 | |
10 days ago | 9 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
-
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.
-
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
-
[P] Build data web apps in Jupyter Notebook with Python only
Sure, at the bottom of our website you can subscribe for newsletter.
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
-
library / framework to test multiple sklearn regression models at once
If you need a simple and fast solution, go with auto-sklearn Maybe a bit more complex, but very powerful was mljar-supervised
- Python AutoML on Tabular Data with FeatureEng, HP Tuning, Explanations, AutoDoc
-
Data Science and full-stack-web development
In my case, I had experience in DS and software engineering. It gives me ability to start a company that works on Data Science tools.
-
Learning Python tricks by reading other people's code. But who?
MLJAR AutoML is a Python package for Automated Machine Learning on tabular data with feature engineering, explanations, and automatic documentation.
-
'start with a simple model'
I recommend trying my AutoML package. You can easily check many different algorithms. Waht is more, the baseline algorithms are checked (major class predictor for classification and mean predictor for regression). The advance of AutoML is that it is really quick. You dont need to write preprocessing code, just call fit method.
automlbenchmark
-
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
Your list excludes most of well-known open-source AutoML tools such as auto-sklearn, AutoGluon, LightAutoML, MLJarSupervised, etc. These tools have been very extensively benchmarked by the OpenML AutoML Benchmark (https://github.com/openml/automlbenchmark) and have papers published, so they are pretty well-known to the AutoML community.
Regarding H2O.ai: Frankly, you don't seem to understand H2O.ai's AutoML offerings.
I'm the creator of H2O AutoML, which is open source, and there's no "enterprise version" of H2O AutoML. The interface is simple -- all you need to specify is the training data and target. We have included DNNs in our set of models since the first release of the tool in 2017. Read more here: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html We also offer full explainability for our models: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/explain.html
H2O.ai develops another AutoML tool called Driverless AI, which is proprietary. You might be conflating the two. Neither of these tools need to be used on the H2O AI Cloud. Both tools pre-date our cloud by many years and can be used on a user's own laptop/server very easily.
Your Features & Roadmap list in the README indicates that your tool does not yet offer DNNs, so either you should update your post here or update your README if it's incorrect: https://github.com/blobcity/autoai/blob/main/README.md#featu...
Lastly, I thought I would mention that there's already an AutoML tool called "AutoAI" by IBM. Generally, it's not a good idea to have name collisions in a small space like the AutoML community. https://www.ibm.com/support/producthub/icpdata/docs/content/...
-
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?
optuna - A hyperparameter optimization framework
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
autokeras - AutoML library for deep learning
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.
PySR - High-Performance Symbolic Regression in Python and Julia
MindsDB - The platform for customizing AI from enterprise data
AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
adanet - Fast and flexible AutoML with learning guarantees.
mljar-examples - Examples how MLJAR can be used
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.