Verified65% confidenceFactTime unknown
Macro-F1对每个类别分别计算F1后取算术平均,Micro-F1先汇总所有类别的TP/FP/FN再统一计算F1
3
Sources
65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
混淆矩阵详解:Precision、Recall与F1分数计算公式及实战应用
bilibili拓跋龙的私人领地
Related Entities
Related Claims
UnverifiedFiorillo v0.5 的宏观 F1(macro-F1)为 0.9248,对比基线的 0.866863% similarUnverified在ONNX中使用INT8权重配合INT4嵌入层的量化组合,相比精度更保守的量化版本,F1分数差异仅约0.00558% similarUnverifiedGTX 10系(sm_61,GP104芯片)刻意阉割FP16性能,需拆分为两个FP32操作,FP16算力仅约为FP32的一半53% similarUnverifiedF100继承了F5的Multi-CAM1300对焦系统和3D矩阵测光,机身重量约785克,价格约为F5的一半53% similarUnverified新系统的F1分数从0.606提升至0.70253% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/20852API
curl https://kongchang.com/api/v1/knowledge/claims/20852MCP
get_claim(id=20852)