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

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  • mljar-supervised

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

  • mljar-examples

    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](

    - 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

    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

    I have some old comparison of closed-source old system

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