Label encoding
Label encoding is a categorical-data transformation that maps each category to a numerical code, which may imply an ordering among categories.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
Ordinal encoding represents categorical values with integer codes that impose an ordered numerical representation.
- How it is used
- Prepares categorical predictors as numeric inputs for machine-learning pipelines and categorical relationship analysis.
- How deep it goes
- Defines arbitrary numeric category assignment and compares model suitability, cardinality limits, and pipeline configuration.
- Examples
- Uses Airbnb neighbourhood, accommodation type, room type, and coordinates features in ColumnTransformer pipelines.
- What you can do after
- Apply it via scikit-learn OrdinalEncoder and ColumnTransformer when configuring categorical feature pipelines.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains label encoding →
Grokking AI Algorithms, Second Edition
Label encoding maps each category to a numerical label, typically an integer in a fixed range.
- How it is used
- Converts rating-related categorical features into numeric inputs for statistical models.
- How deep it goes
- Defines numeric labels and explains when to choose them over one-hot encoding, without giving an implementation workflow.
- Examples
- Contrasts diamond Cut ratings with unranked Origin values and compares them with reward-valued neural-network inputs.
- What you can do after
- Practice/perform categorical data preparation by labeling ratings and choosing one-hot encoding for categories without a ranking.
Read how Grokking AI Algorithms, Second Edition explains label encoding →
Ensemble Methods for Machine Learning
Ordinal encoding maps each category of a categorical variable to a single numeric value, optionally preserving an inherent category order.
- How it is used
- Converts categorical features into numeric inputs before training machine-learning models.
- How deep it goes
- Defines mappings, demonstrates explicit category orders, and stops short of detailed encoding internals.
- Examples
- Uses color and size mappings, an explicit OrdinalEncoder configuration, encoded arrays, and ensemble comparisons.
- What you can do after
- Apply it via scikit-learn's OrdinalEncoder and compare its model performance against alternative encodings.
Read how Ensemble Methods for Machine Learning explains label encoding →
Outlier Detection in Python
Ordinal encoding is a categorical encoding technique that maps discrete categories to integer codes representing an order.
- How it is used
- Prepares categorical features for numeric outlier detectors when mixed data cannot be processed directly.
- How deep it goes
- Explains integer mapping and detector-specific behavior, but stops short of prescribing one universally best encoding.
- Examples
- Uses Color mappings, Department frequencies, and categorical bins in HBOS to demonstrate representation and detector effects.
- What you can do after
- Apply ordinal encoding when preparing categorical features for compatible unsupervised outlier detectors.
Read how Outlier Detection in Python explains label encoding →
Related concepts
- categorical encodingkind of · Categorical encoding transforms discrete category values into numerical representations, such as separate binary indicators or vectors with one active position per category.
- one-hot encodingalternative to · One-hot encoding represents each category as a binary vector or set of indicators with exactly one active component identifying that category.
- OrdinalEncoderused by · OrdinalEncoder is a scikit-learn transformer that maps categorical feature values to integer codes representing their ordered categories.
- scikit-learnused by · scikit-learn is an open-source Python library providing tools for classical machine learning, including algorithms, preprocessing, model selection, and evaluation.
- biasproduces · Bias is a systematic deviation or tendency in data, measurements, estimates, judgments, or outputs that skews them relative to a relevant target or impartial baseline.
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