Show HN: Mljar Automated Machine Learning for Tabular Data (Explanation,AutoDoc)

• mljar-supervised

  51 2,929 8.5 Python

  Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

  

• mljar-examples

  2 58 3.3 Jupyter Notebook

  Examples how MLJAR can be used

  

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.

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• automlbenchmark

  3 379 6.9 Python

  OpenML AutoML Benchmarking Framework

  

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

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