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Prepping tables for machine learning

License: BSD 3-Clause "New" or "Revised" License

Shell 0.87% JavaScript 3.11% Python 92.44% CSS 1.59% HTML 1.99%

skrub's Introduction

skrub

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.