Category_encoders
category_encoders is a Python library providing scikit-learn-compatible transformations that encode categorical variables as numerical features.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
category_encoders is a Python library providing scikit-learn-compatible encoders for categorical variables, including target and ordinal encoders.
- How it is used
- Applies target-based transformations to high-cardinality categorical features before model training.
- How deep it goes
- Explains target statistics, smoothing, and encoder selection, then stops short of full implementation details.
- Examples
- Uses neighborhood values, gridded latitude–longitude coordinates, TargetEncoder, column transformation, and five-fold validation.
- What you can do after
- Apply TargetEncoder in a scikit-learn-compatible preprocessing pipeline and tune or replace its smoothing strategy.
Ensemble Methods for Machine Learning
category_encoders is a Python library that provides encoders for converting categorical variables into numerical representations compatible with scikit-learn workflows.
- How it is used
- Supports categorical preprocessing before training scikit-learn-compatible models, including LightGBM and XGBoost.
- How deep it goes
- Demonstrates pipeline construction, column selection, fitting, transformation, and encoder comparison, but does not detail encoder internals.
- Examples
- Uses the Australian Credit Approval dataset to compare one-hot, greedy target, and leave-one-out target encoding.
- What you can do after
- Apply it via scikit-learn pipelines to encode categorical columns and evaluate downstream ensemble models.
Read how Ensemble Methods for Machine Learning explains category_encoders →
Related concepts
- OneHotEncoderuses · OneHotEncoder is a transformer that represents each category of a categorical feature with a separate binary indicator feature.
- categorical datauses · Categorical data consists of values representing membership in a discrete set of named or ordered categories rather than measured numerical magnitudes.
- categorical encodinguses · Categorical encoding transforms discrete category values into numerical representations, such as separate binary indicators or vectors with one active position per category.
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