How deeply does Grokking AI Algorithms, Second Edition cover label encoding?

Defines numeric labels and explains when to choose them over one-hot encoding, without giving an implementation workflow.

Label encoding

As explained in Grokking AI Algorithms, Second Edition

Label encoding maps each category to a numerical label, typically an integer in a fixed range.

Label encoding replaces each category with a number. It lets a model receive numeric input instead of strings such as “Fair” or “Good.” The choice of numbers matters: a model may treat a larger label as carrying greater weight. section 8.9

Encoding a diamond rating

A possible encoding for Cut is Fair → 0, Good → 1, Very Good → 2, Premium → 3, and Ideal → 4. These labels follow a rating order. By contrast, assigning numbers to a diamond’s Origin would suggest that one source location ranks above another, even though none does in this dataset. section 8.9

Use label encoding for ratings or rating-related categories. For categories with no meaningful ranking, use one-hot encoding instead: it gives each category its own indicator rather than a potentially misleading number. section 8.9

Table 8.6 The diamond dataset with one-hot-encoded values

	Carat	Cut: Fair	Cut: Good	Cut: Very Good	Cut: Premium	Cut: Ideal
1	0.30	0	1	0	0	0
2	0.41	0	0	0	0	1
3	0.75	0	0	1	0	0
4	0.91	0	0	0	0	0
5	1.20	1	0	0	0	0
6	1.21	0	1	0	0	0
7	1.31	0	0	0	1	0
8	1.50	0	0	0	1	0
9	1.74	0	0	1	0	0
10	1.83	1	0	0	0	0
11	1.96	1	0	0	0	0
12	1.19	0	0	0	1	0

The Cut columns show the one-hot alternative: each diamond has a 1 under its category and 0 under the others. section 8.9

A related approach, scalar encoding, assigns values that express relative impact—for example, positive for a goal and negative for a collision. It is compact, but can mislead a neural network if the assumed relationships between categories do not hold. section 10.11

How Grokking AI Algorithms, Second Edition covers it

Cover of Grokking AI Algorithms, Second Edition

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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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Ensemble Methods for Machine LearningOrdinal encoding maps each category of a categorical variable to a single numeric value, optionally preserving an inherent category order.
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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.

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.