F-Measureは、別名 F1スコア, is a statistical measure used to assess the performance of a classification model, particularly in scenarios with 不均衡なデータセット. It combines two critical 評価指標: precision and recall.
精度 refers to the number of true positive results divided by the sum of true positive and 誤陽性 results. It indicates how many of the predicted positive instances are actually positive. 想起, on the other hand, is the number of true positive results divided by the sum of true positive and false negative results, measuring how many of the actual positive instances were correctly identified by the model.
F値は次の式を用いて計算されます：
F1 = 2 * (適合率 * 再現率) / (適合率 + 再現率)
This formula creates a single score that balances both precision and recall, providing a more comprehensive measure of a model’s accuracy than either metric alone. The F-Measure ranges from 0 to 1, where 1 indicates perfect precision and recall, and 0 indicates the worst performance.
F-Measureは、次のようなアプリケーションで特に役立ちます 情報検索, medical diagnosis, and any other field where the cost of false positives and false negatives varies significantly. By focusing on both precision and recall, the F-Measure helps practitioners select models that are better suited to their specific needs and priorities.