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thesis3
PPTX, PDF
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Precision and Recall
AI-enhanced description
文档介绍了精确率和召回率的概念,以及它们在计算机识别图像中的应用。使用真实正类、假正类、真实负类和假负类的示例来阐述这些指标,并提供了相关的计算公式。最后提到F1分数作为精确率和召回率的调和平均值。
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Precision and Recall
1.
Precision and Recall
2.
正類, 相關 負類,
無關 有挑到 Retrieved True Positives, TP 正類有挑到 False Positives, FP 負類也挑到 沒挑到 Not retrieved False Negatives, FN 正類沒挑到 True Negatives, TN 負類沒挑到
3.
正類, 相關 負類,
無關 有挑到 Retrieved True Positives, TP 5 False Positives, FP 3 沒挑到 Not retrieved False Negatives, FN 7 True Negatives, TN 負類沒挑到 12隻狗和一些貓的照片,電腦識別狗的照片有8張,但只有5張真的是狗(True Positives),3張是貓(False Positives),Precision是5/8,Recall是5/12
4.
• Precision =
識別出來,有幾張是對的。TP/(TP+FP) • Recall = 識別出來對的,佔全部對的幾%(未識別出也算進來)。 TP/(TP+FN) • Accuracy=(TP+TN)/(TP+TN+FP+FN),挑正確+不挑錯 除 全部有挑和 沒挑。 • F1= 2/((1/Precision)+(1/Recall)) harmonic mean of Precision and Recall,將兩個比例融合在一起,因為分子相同,故可倒數相加。
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