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

33 点作者 pplonski86超过 4 年前

3 条评论

pplonski86超过 4 年前
The creator here. I&#x27;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:<p>- Operates in three modes: Explain, Perform, Compete.<p>- `Explain` is for data exploratory and checking the default performance (without HP tuning). It has Automatic Exploratory Data Analysis.<p>- `Perform` is for building production-ready models (HP tuning + ensembling).<p>- `Compete` is for solving ML competitions in limited time amount (HP tuning + ensembling + stacking).<p>- All ML experiments have automatic documentation which creates Markdown reports ready to commit to the repo (<a href="https:&#x2F;&#x2F;github.com&#x2F;mljar&#x2F;mljar-examples&#x2F;tree&#x2F;master&#x2F;Income_classification&#x2F;AutoML_1#automl-leaderboard" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;mljar&#x2F;mljar-examples&#x2F;tree&#x2F;master&#x2F;Income_c...</a>).<p>- The package produces extensive explanations: decision tree visualization, feature importance, SHAP explanations, advanced metrics values.<p>- 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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daemonk超过 4 年前
This really cool. The examples in github doesn&#x27;t seem to show any defining of feature types (categorical, binary, continuous, etc). Is there automatic detection of feature type?
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alesio超过 4 年前
Automatic documentation seems to be an advatage. But what about explainabity?
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