(95% confidence interval [CI]: 0.947-0.982), an accuracy of 0.926 (95% CI: 0.925-0.926), and an F1 score of 0.924, outperforming other traditional... Xin R,Yang J,Chen Y - European Heart Journal 被引量: 0发表: 2024年 加载更多来源...
imblearn.metrics.macro_averaged_mean_absolute_error() raises an exception when not all classes are represented in the ground truth. Thought I'd flag this because a similar function in sklearn behaves differently to imbalanced-learn. That function is f1_score with macro averaging. Here's an exam...