Mean target encoding
Mean target encoding is a categorical encoding technique that replaces each category with the mean of its associated target values.
Ensemble Methods for Machine Learning
Greedy target encoding uses target-derived statistics computed from training data to encode categorical values, potentially introducing target leakage when the encoded examples contribute to those statistics.
- How it is used
- Encodes categorical features numerically for supervised classification and regression pipelines.
- How deep it goes
- Defines per-category target means, walks through a numeric example, and contrasts leakage mitigations without deriving their implementations.
- Examples
- Uses T-shirt color and cost means, Australian Credit Approval pipelines, and encoder comparisons with random forest, LightGBM, and XGBoost.
- What you can do after
- Apply it via category_encoders and evaluate it against alternative encoders with classification metrics.
Read how Ensemble Methods for Machine Learning explains mean target encoding →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A feature encoding technique that replaces each category with a statistic computed from the target values associated with that category.
- How it is used
- Reduces high-cardinality categorical features to numeric target-derived features for tabular machine-learning models.
- How deep it goes
- Defines regression and classification statistics, explains smoothing and overfitting risk, and demonstrates pipeline integration without deriving the estimator.
- Examples
- Uses IDs, zip codes, product names, geographical names, and a binned latitude-longitude coordinates feature.
- What you can do after
- Apply it via category_encoders TargetEncoder with selected columns and a tunable smoothing parameter.
Machine Learning System Design
Target encoding is a categorical-variable encoding technique that replaces each category with a statistic of the target values associated with that category.
- How it is used
- Transforms categorical features into model inputs using target-associated category statistics.
- How deep it goes
- Defines regularized category-wise target means and flags leakage, but does not detail the encoding algorithm.
- Examples
- Uses mixed tabular features in ETA prediction and contrasts mean target encoding with one-hot, ordinal, and hashing transformations.
- What you can do after
- Evaluate target-encoded features while checking leakage and applying nested cross-validation.
Read how Machine Learning System Design explains mean target encoding →
Related concepts
- TargetEncoderimplemented by · TargetEncoder is an encoding method that replaces categorical values with statistics computed from the target variable, often using smoothing.
- target leakingproduces · Target leakage is the inclusion or use of information derived from the prediction target that would be unavailable when making predictions.
- supervised encodingkind of · Supervised encoding is a categorical-feature encoding technique that uses target values when deriving category representations.
- labeluses · A label is a value or annotation associated with an item that identifies its target category, outcome, or reference answer for learning or evaluation.
- embeddingalternative to · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
- high-cardinality categorical featureaddresses · A high-cardinality categorical feature is a categorical feature with many distinct values relative to its dataset or analysis context.
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