再現率

Recall, also known as sensitivity or the true positive rate, is a fundamental performance metric in machine learning that measures the ability of a model to identify all relevant instances within a dataset. In the context of object detection or classification, it specifically answers the question: "Out of all the actual positive cases, how many did the model correctly find?" Achieving high recall is critical in scenarios where missing a positive instance—often referred to as a false negative—carries significant consequences. Unlike accuracy, which can be misleading when dealing with imbalanced data, recall provides a focused view on the model's effectiveness at "capturing" the target class.

高いリコールの重要性

In many artificial intelligence applications, the cost of failing to detect an object is far higher than the cost of a false alarm. A model optimized for recall minimizes false negatives, ensuring that the system casts a wide enough net to catch potential threats, anomalies, or critical conditions. This often involves a trade-off, as increasing recall can sometimes lead to a lower precision score, meaning the model might flag more non-relevant items as positive. Understanding this balance is key to developing robust machine learning solutions.

実際のアプリケーション

リコールは多くの安全上重要なAIソリューションの主要指標である。感度が優先される代表的な事例を2つ挙げる：

• 医療診断： 医療画像解析（疾患の早期兆候をスクリーニングするX線など）において、高い再現率は絶対条件である。医療システムで腫瘍detect にAIを使用する場合、悪性腫瘍を見逃すよりも、結果的に良性である疑わしい影を誤検知（偽陽性）としてフラグを立てる方がはるかに望ましい。 医師はこれらのツールを安全装置として依存し、潜在的な健康リスクを見逃さないことを保証している。
• セキュリティと監視： セキュリティ警報システムの主な目的は、 detect 侵入試みをdetect ことです。 高再現率に最適化されたシステムは、 制限区域に人が侵入した場合に警報が作動することを保証します。 野生生物による誤作動が時折発生する可能性はありますが、 実際のdetect システムよりも この方式が望ましいのです。 こうしたシナリオにおける物体検知モデルは、 潜在的な脅威に対する感度を最大化するよう調整されています。

Recall vs. Precision

It is essential to distinguish recall from its counterpart, precision. While recall measures the quantity of relevant cases found (completeness), precision measures the quality of the positive predictions (exactness).

• Recall: Focuses on avoiding missed detections. "Did we find all the apples?"
• Precision: Focuses on minimizing false alarms. "Are all the things we called apples actually apples?"

These two metrics often share an inverse relationship, visualized through a Precision-Recall curve. To evaluate the overall balance between them, developers often look at the F1-score, which is the harmonic mean of both. In imbalanced datasets, looking at recall alongside the confusion matrix gives a much clearer picture of performance than accuracy alone.

Ultralytics YOLOリコール測定

When training models like the cutting-edge YOLO26, recall is automatically computed during the validation phase. The framework calculates recall for each class and the mean Average Precision (mAP), helping developers gauge how well the model finds objects.

Pythonを使用すれば、学習済みモデルの検証や再現率メトリクスの確認を簡単に行えます。以下のスニペットは、モデルを読み込み標準データセットでの性能を確認する方法を示しています：

from ultralytics import YOLO

# Load a pretrained YOLO26 model
model = YOLO("yolo26n.pt")

# Validate the model on the COCO8 dataset
# The results object contains metrics like Precision, Recall, and mAP
metrics = model.val(data="coco8.yaml")

# Access and print the mean recall score for box detection
print(f"Mean Recall: {metrics.results_dict['metrics/recall(B)']:.4f}")

This code utilizes the Ultralytics API to run validation. If the recall is lower than required for your project, you might consider techniques like data augmentation to create more varied training examples or hyperparameter tuning to adjust the model's sensitivity. Using the Ultralytics Platform can also streamline the process of managing datasets and tracking these metrics over multiple training runs.

Improving Model Recall

To boost a model's recall, data scientists often adjust the confidence threshold used during inference. Lowering the threshold makes the model more "optimistic," accepting more predictions as positive, which increases recall but may decrease precision. Additionally, collecting more diverse training data helps the model learn to recognize hard negatives and obscure instances. For complex tasks, employing advanced architectures like Transformer blocks or exploring ensemble methods can also improve the system's ability to detect subtle features that simpler models might miss.