How deeply does Ensemble Methods for Machine Learning cover label encoding?

Defines mappings, demonstrates explicit category orders, and stops short of detailed encoding internals.

Label encoding

As explained in 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.

Label encoding, also called ordinal encoding, converts each categorical value into one integer so machine-learning models can consume it as a numeric feature. It is unsupervised: the mapping uses feature values, not target labels. section 8.1 section 8.1.2

Build an encoding by following these decisions:

List the allowed categories and assign each a distinct integer, such as red → 0, blue → 1, and green → 2. section 8.1.2
Preserve meaningful order when the feature is ordinal: S, M, L, XL can become 0, 1, 2, 3, or the reverse. section 8.1.2
Treat nominal mappings cautiously: assigning integers creates an apparent ordering even when categories have no relationship. section 8.1.2 section 8.5
Fit preprocessing on training data and apply the learned transformation consistently to later data. section 8.1.4

Encoding color and size

Given rows such as [red, M] and [blue, XL], specify color as [red, yellow, green, blue] and size as [XL, L, M, S]. scikit-learn's OrdinalEncoder then produces [0, 2] for [red, M] and [3, 0] for [blue, XL]. Unobserved categories, such as yellow, still receive their specified value.

Label encoding is compact because one feature remains one numeric column, but it can distort relationships for nominal variables. Compare it with one-hot encoding and evaluate the complete preprocessing-model combination using suitable metrics, especially when classes are imbalanced. section 8.1.2 section 8.3.3

How Ensemble Methods for Machine Learning covers it

Cover of Ensemble Methods for Machine Learning

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIOrdinal encoding represents categorical values with integer codes that impose an ordered numerical representation.
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Grokking AI Algorithms, Second EditionLabel encoding maps each category to a numerical label, typically an integer in a fixed range.
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Outlier Detection in PythonOrdinal encoding is a categorical encoding technique that maps discrete categories to integer codes representing an order.

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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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