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Dataset split

As explained in Natural Language Processing in Action, Second Edition

A train-test split partitions examples into separate subsets for model fitting and evaluation.

A dataset split separates examples for model fitting from examples reserved for evaluation. In a toxic-comment classifier, an 80/20 split provides substantial training data while preserving a held-out check on performance. section 9.3.4

Toxic-comment classification

Keep comment text in X and six toxicity labels in y, then use a fixed random seed to create reproducible training and evaluation subsets. The resulting data contains 127,656 training rows and 31,915 evaluation rows.

How Natural Language Processing in Action, Second Edition covers it

How deep it goes
Demonstrates an 80/20 implementation with reproducibility and downstream dataset construction, but does not discuss alternative splitting strategies.
Examples
Uses a toxic-comment DataFrame, six binary toxicity labels, scikit-learn train_test_split, and separate training and evaluation DataFrames.
What it assumes
Assumes labeled examples in a pandas DataFrame and teaches the partitioning step before model training.
What you can do after
Apply it via scikit-learn to create reproducible subsets and evaluate a fine-tuned classifier on held-out data.
How it connects
Connects dataset splitting with model evaluation, fine-tuning, validation sets, multi-label classification, and class imbalance.

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This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.