-
mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
-
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.
-
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
Related posts
-
MLJAR Automated Machine Learning for Tabular Data (Stacking, Golden Features, Explanations, and AutoDoc)
-
Show HN: Supertree – interactive visualization of decision trees in Python
-
Fairness in machine learning
-
Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
-
Python AutoML on Tabular Data with FeatureEng, HP Tuning, Explanations, AutoDoc