OrdinalEncoder

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover OrdinalEncoder?

Defines ordinal transformation and compares model fit, but stops short of implementation syntax or parameter details.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

OrdinalEncoder is a scikit-learn transformer that maps categories to numeric ordinal codes.

OrdinalEncoder converts categorical string values into ordered numeric codes so machine-learning algorithms can process them. It is especially suitable for complex tree-based models such as random forests and gradient-boosted decision trees, which can split the coded feature into useful partitions. section 6.1.3

Choose ordinal encoding with these trade-offs in mind:

Prefer one-hot encoding for many linear and tree-based modeling cases when categories are manageable. section 6.1.3
Treat high-cardinality features such as IDs, ZIP codes, product names, and geographical names cautiously: ordinal codes may not form meaningful tree partitions. section 6.1.3
Consider target encoding for high-cardinality categoricals when one-hot and ordinal representations create modeling or memory problems. section 6.1.3

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

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Related concepts

label encodinguses · Label encoding is a categorical-data transformation that maps each category to a numerical code, which may imply an ordering among categories.
categorical encodingimplements · Categorical encoding transforms discrete category values into numerical representations, such as separate binary indicators or vectors with one active position per category.
isolation forestused by · Isolation Forest is an ensemble anomaly-detection algorithm that identifies unusual observations by recursively isolating them through random partitioning.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.