F-score

As explained in Machine Learning Algorithms in Depth

The F1 score is the harmonic mean of precision and recall.

The F1 score summarizes classification performance at a fixed threshold as the harmonic mean of precision and recall: F1 = 2PR/(P + R), where P = TP/(TP + FP) and R = TP/(TP + FN). section 5.2

Figure 5.4 Receiver operating characteristic (ROC) plot (left) and precision-recall plot (right)

A precision-recall plot shows the tradeoff as the classification threshold varies; the F1 score summarizes one selected threshold. section 5.2

How Machine Learning Algorithms in Depth covers it

How deep it goes

Defines precision and recall, then gives the harmonic-mean formula without further derivation or implementation.

Examples

Uses precision-recall plots and threshold-specific performance summaries as the concrete evaluation setting.

What you can do after

Evaluate classification performance by combining precision and recall into one threshold-specific statistic.

How it connects

Connects precision, recall, classification thresholds, precision-recall curves, and confusion-matrix counts.

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Related concepts

• 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 Algorithms in Depth. The section links point to the parts of the book it draws on.