Term

• True Positives (TP): should be TRUE, you predicted TRUE
• False Positives (FP): should be FALSE, you predicted TRUE
• True Negative (TN): should be FALSE, you predicted FALSE
• False Negatives (FN): should be TRUE, you predicted FALSE

个人看来，TP和TN都很好理解，二者反映了模型对于预测的准确性。FP和FN有点容易记不太顺，所以使用一个例子就很好去区分：
张三去看病，是否得了癌症, FP=>误诊, FN=>没查出来。

Accuracy - 准确率

What percentage of my predictions are correct? 模型预测的正确性

• Good for single label, binary classifcation.
• Not good for imbalanced datasets. 受数据不均衡影响很大
  If, in the dataset, 99% of samples are TRUE and you blindly predict TRUE for everything, you’ll have 0.99 accuracy, but you haven’t actually learned anything.

Precision - 查准率

Of the points that I predicted TRUE, how many are actually TRUE? 模型预测的正例中。真实正例的比例

• Good for multi-label / multi-class classification and information retrieval
• Good for unbalanced datasets

Recall - 查全率

Of all the points that are actually TRUE, how many did I correctly predict 真实正例中，模型预测为正的比例

• Good for multi-label / multi-class classification and information retrieval
• Good for unbalanced datasets

F1

Can you give me a single metric that balances precision and recall? F1-score (alternatively, F1-Measure), is a mixed metric that takes into account both Precision and Recall. 将查全与查准都考虑进去的F1

• Gives equal weight to precision and recall
• Good for unbalanced datasets

AUC (Area under ROC Curve)

Is my model better than just random guessing?
…待定。。。

源地址： https://queirozf.com/entries/evaluation-metrics-for-classification-quick-examples-references#precision