Category-Encoders
11 posts
HashingEncoder: Tackling Extreme Cardinality with the Hashing Trick
Explore HashingEncoder: learn how it handles extreme cardinality, its mechanism, ideal use cases, and implementation with the category_encoders library.
BinaryEncoder: The Space-Efficient Alternative to One-Hot Encoding
Explore the BinaryEncoder from category_encoders: a space-efficient alternative to one-hot encoding. Learn how it works, when to use it, and see implementation.
OrdinalEncoder: When Order Matters in Categorical Data
Explore OrdinalEncoder for categorical data where order matters. Learn how it works, its benefits, and implementation using the category_encoders library.
OneHotEncoder: The Workhorse of Categorical Encoding
A comprehensive guide to OneHotEncoder in category_encoders, exploring its core functionality, advantages, and practical limitations in machine learning.
Category Encoders v1.2.8 Release
Announcing Category Encoders v1.2.8! This release includes important bugfixes and introduces new features like optional category names in output columns.
Category Encoders published in JOSS
Announcing the category_encoders Python package publication in JOSS. Discover the peer-review process and citation details.
Category Encoders v1.2.5 Release
Category Encoders v1.2.5 brings community updates including stable binary/BaseN encoding, new leave-one-out encoding, and pandas compatibility fixes.
Category Encoders v1.2.4 Release
Category Encoders v1.2.4 is out! Includes pandas categorical type support, improved missing value handling, better error messages, BaseN fixes, and docs.
BaseN Encoding Grid Search in Category Encoders
Explore category_encoders' BaseN encoder for representing categorical data. Learn how to use scikit-learn's grid search to find the optimal encoding base.
Category Encoders accepted into scikit-learn-contrib
Category Encoders, a Python library for encoding categorical variables, has been accepted into the scikit-learn-contrib ecosystem. A project milestone!