Prepping tables for machine learning
Project description
skrub (formerly dirty_cat) is a Python library that facilitates prepping your tables for machine learning.
If you like the package, spread the word and ⭐ this repository! You can also join the discord server.
Website: https://skrub-data.org/
What can skrub do?
The goal of skrub is to bridge the gap between tabular data sources and machine-learning models.
skrub provides high-level tools for joining dataframes (Joiner, AggJoiner, …), encoding columns (MinHashEncoder, ToCategorical, …), building a pipeline (TableVectorizer, tabular_learner, …), and more.
>>> from skrub.datasets import fetch_employee_salaries >>> dataset = fetch_employee_salaries() >>> df = dataset.X >>> y = dataset.y >>> df.iloc[0] gender F department POL department_name Department of Police division MSB Information Mgmt and Tech Division Records... assignment_category Fulltime-Regular employee_position_title Office Services Coordinator date_first_hired 09/22/1986 year_first_hired 1986
>>> from sklearn.model_selection import cross_val_score >>> from skrub import tabular_learner >>> cross_val_score(tabular_learner('regressor'), df, y) array([0.89370447, 0.89279068, 0.92282557, 0.92319094, 0.92162666])
See our examples.
Installation
skrub can easily be installed via pip or conda. For more installation information, see the installation instructions.
Contributing
The best way to support the development of skrub is to spread the word!
Also, if you already are a skrub user, we would love to hear about your use cases and challenges in the Discussions section.
To report a bug or suggest enhancements, please open an issue and/or submit a pull request.
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