The harmonic mean of precision and recall used to evaluate classification performance.
F-score is the harmonic mean of precision and recall, giving a single measure for evaluating classification performance. It is useful when both types of correctness matter. section 1.5
Comparing tabular-data models
On TABLET prediction tasks, XGBoost trained on the full dataset averaged an F1 score of 0.94, compared with 0.68 for ChatGPT and 0.66 for Flan-T5 11b using four-shot examples. section 1.5
How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it
How deep it goes
Defines the metric and reports benchmark comparisons, but does not derive its formula or explain implementation details.
Examples
Compares XGBoost, ChatGPT, and Flan-T5 11b on TABLET prediction tasks using average F1 scores.
What it assumes
Assumes familiarity with classification, precision, and recall while defining F-score as their harmonic mean.
What you can do after
Evaluate and compare classification models with a combined precision-and-recall score.
How it connects
Connects classification performance with precision, recall, tabular data, XGBoost, and large language models.
Deep Learning with PyTorch, Second EditionA harmonic-mean classification metric that balances precision and recall, with the F-score parameter changing the relative emphasis between them.
precisionuses · Precision is the proportion of items identified as positive, selected, or retrieved that satisfy the relevant correctness or relevance criterion.
recalluses · Recall is the fraction of relevant, positive, or target outcomes that a system successfully identifies, retrieves, or captures.
evaluationused by · Evaluation is the systematic assessment of a system, result, or approach against relevant criteria, evidence, or objectives.
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.
