这种情况下，选用macro-f1与micro-f1都差不多，其中macro-f1与weight-f1值是一样的。但这里macro-f1...
FN和FP的数量，再计算F1'macro':Calculate metrics for each label, and find their unweighted mean. ...
④'macro': 对每一类别的f1_score进行简单算术平均(unweighted mean), with assumption that all classes are equally important。 Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ⑤'weighted': 对每一类别的f1_score进行加权平均,权重为各...
Avg. Hourly Earnings −2.370 −0.838 0.004 22.9 1995 Building Permits −0.649 −0.757 0.004 6.9 846 from 2004 GDP Def_A −1.313 −0.508 0.005 13.4 1332 Quarterly from 2001 WholeSale Inventories −0.303 −0.468 0.001 7.0 571 Unit Labor Cost_F −0.262 −0.132 0.000 7.6 70...
Potential outliers were identified and a comparison made of their Grubbs statistic ((xout-xavg)/s) to a Grubbs critical value, which is based on the number of samples. For data points within each study that were identified as significant outliers, the effect of their removal on the final ...
macro avg:宏平均,计算方式为每个类型的算术平均,我们以precision的macro avg为例,上述输出结果中precision的macro avg值的计算方式为:(0.99+0.98)/2 weighted avg:加权平均,是用每个类型样本数量与对应权重相乘再除以所有类别的样本总数,以precision的weighted avg 计算方式为:(0.99×89+0.98×48)/(89+48),即(0.9...
micro f1不需要区分类别,直接使用总体样本的准召计算f1 score。 该样本的混淆矩阵如下: precision = 5/(5+4) = 0.5556 recall = 5/(5+4) = 0.5556 F1 = 2 * (0.5556 * 0.5556)/(0.5556 + 0.5556) = 0.5556 下面调用sklearn的api进行验证
macro其实就是先计算出每个类别的F1值，然后去平均，比如下面多分类问题，总共有1,2,3,4这4个类别，...
marco-F1：计算方法：将所有类别的Precision和Recall求平均，然后计算F1值作为macro-F1；使用场景：没有...