automlbenchmark VS mljar-examples

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.

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/...

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

All ML experiments have automatic documentation that creates Markdown reports ready to commit to the repo (example1, example2).

The creator here. I'm working on AutoML since 2016. I think that latest release (0.7.15) of MLJAR AutoML is amazing. It has ton of fantastic features that I always want to have in AutoML:

- Operates in three modes: Explain, Perform, Compete.

- `Explain` is for data exploratory and checking the default performance (without HP tuning). It has Automatic Exploratory Data Analysis.

- `Perform` is for building production-ready models (HP tuning + ensembling).

- `Compete` is for solving ML competitions in limited time amount (HP tuning + ensembling + stacking).

- All ML experiments have automatic documentation which creates Markdown reports ready to commit to the repo ([example](https://github.com/mljar/mljar-examples/tree/master/Income_c...)).

- The package produces extensive explanations: decision tree visualization, feature importance, SHAP explanations, advanced metrics values.

- It has advanced feature engineering, like: Golden Features, Features Selection, Time and Text Transformations, Categoricals handling with target, label, or one-hot encodings.