upgini
powershap
| upgini | powershap | |
|---|---|---|
| 16 | 1 | |
| 290 | 178 | |
| 2.4% | 2.8% | |
| 9.1 | 3.5 | |
| 4 days ago | 11 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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.
upgini
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The fastest way to improve quality of ML model on tabular data
web: https://upgini.com
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How to enrich ML models with open data for free: an in-depth review of 5 python libraries
The code is on GitHub.
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How I complete my email addresses lists with demographic insights with Python
Now let’s launch the search for gender-correlated features. For this operation, I will need a free API token that I get after sign up on upgini.com. The API token is free and using to search features by personal keys such as email, phone number, and IP address.
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[OC] Divorced relationship status share of users at Facebook
Source: Upgini database Made with MapChart
- GitHub - searching open and public data through autoML. Please give a Star on GitHub
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[P] Upgini 1.0 is released (a Python library for data search through autoML )
Full release notes: https://github.com/upgini/upgini
- Need your help with GitHub
- Upgini.com: Public data search engine for ML that helps DS to reach best accuracy with external features
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Python library for free data search & enrichment
GitHub
powershap
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[R] PowerShap: A power-full Shapley feature selection method.
We are glad to present our novel shap-based wrapper feature selection method called PowerShap! This method uses statistical hypothesis testing and power calculations on Shapley values, enabling fast and intuitive wrapper-based feature selection. The complete library and methods are fully compatible with Sklearn, LightGBM, CatBoost, and more are coming in further following releases and the library can be found here: https://github.com/predict-idlab/powershap! The library is open-source and usable out-of-the-box as shown in the video!
What are some alternatives?
NitroFE - NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
cascade - Lightweight and modular MLOps library targeted at small teams or individuals
featuretools - An open source python library for automated feature engineering
evalml - EvalML is an AutoML library written in python.
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
NVTabular - NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
LeanDojoChatGPT - ChatGPT plugin for theorem proving in Lean
scikit-learn - scikit-learn: machine learning in Python
fibs-reporter - Automatically generate a pdf report containing feature importance, baseline modelling, spurious correlation detection, and more, from a single command line input for any given ML CSV file
Sklearn-genetic-opt - ML hyperparameters tuning and features selection, using evolutionary algorithms.
atariemailarchive-data - A structured dataset of emails sent at Atari from 1983 to 1992.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation