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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 alw
by pplonski86 6y ago
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 (https://github.com/mljar/mljar-examples/tree/master/Income_classification/AutoML_1#automl-leaderboard 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.
- cweill 6y agoThis is really cool! I'm curious to see how this performs against other AutoML systems like autogluon, autokeras, and AutoML Tables. Do you have any model quality benchmarks on standard research tabular datasets (UCI, OpenML, PMLB) or Kaggle competitions?
- pplonski86 6y agoI'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 https://github.com/openml/automlbenchmark I have some old comparison of closed-source old system https://mljar.com/blog/automl-comparison/ https://mljar.com/blog/automl-comparison/ but the open source version is much better and have more features.
- dfraser992 6y agoI will certainly be taking a good look at this! My pandemic project was building a system that processes raw text documents into different sets of features (BOW, sentiment, etc) and I ended up developing a generic library for easily processing a database table into the appropriate sort of numpy array. So it has started to veer into AI flavored automl... hopefully I could contribute something to your project, but it seems like you've got quite a lot there already.