humble-benchmarks
mljar-examples
| humble-benchmarks | mljar-examples | |
|---|---|---|
| 1 | 2 | |
| 4 | 58 | |
| - | - | |
| 0.0 | 3.3 | |
| about 2 years ago | 5 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
humble-benchmarks
mljar-examples
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MLJAR Automated Machine Learning for Tabular Data (Stacking, Golden Features, Explanations, and AutoDoc)
All ML experiments have automatic documentation that creates Markdown reports ready to commit to the repo (example1, example2).
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Show HN: Mljar Automated Machine Learning for Tabular Data (Explanation,AutoDoc)
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.
What are some alternatives?
SciTS - A tool to benchmark Time-series databases
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
PyImpetus - PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
60-Days-of-Data-Science-and-ML - 60 Days of Data Science and ML
automlbenchmark - OpenML AutoML Benchmarking Framework
homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
machine_learning_basics - Plain python implementations of basic machine learning algorithms
python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource