liveBook · Manning

F-score

As explained in Financial AI in Practice

The F1 score is the harmonic mean of precision and recall for a classification model.

The F1 score is the harmonic mean of precision and recall. In fraud detection, it provides one balanced metric when the business costs of false positives and false negatives are roughly equal. section 8.1.1

Why accuracy can mislead

A model that approves every transaction can achieve 99% accuracy while catching no fraud. Precision, recall, and F1 focus evaluation on fraud detection outcomes instead. section 8.1.1

How Financial AI in Practice covers it

How deep it goes
Defines F1 as the harmonic mean of precision and recall, and limits its use to cases with roughly equal error costs.
Examples
Contrasts a model with 99% accuracy that catches no fraud against metrics focused on fraud detection outcomes.
What you can do after
Evaluate fraud models with a balanced precision-recall metric when false-positive and false-negative costs are roughly equal.
How it connects
Connects precision and recall with fraud-model evaluation, alongside accuracy and area under the precision-recall curve.
Cover of Financial AI in Practice
liveBookmanning.com
Read Financial AI in Practice on liveBook Get the book at manning.com

Also explained in

Compare how 14 books explain f-score →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.