翻译：
What are the metrics when evaluating the performance of an AI (artificial intelligence) model?
模型评估指标是衡量AI模型预测质量的一组量化口径，构成上包括准确率、精确率、召回率以及F1值（F1 score，精确率与召回率的调和平均），解决的是模型效果好坏缺乏统一可比标准的问题，并不解决训练数据偏差造成的评估结论失真与实际业务收益落差问题。
Model evaluation index is an important index to measure model performance, and the evaluation index of different models is different. Here are some metrics commonly used in machine learning models:
When evaluating the performance of an AI (artificial intelligence) model, the model evaluation indicators are:
1. Accuracy: Accuracy refers to the accuracy rate of model prediction. The formula is: Accuracy = the number of samples predicted correctly/the total number of samples.
2. Accuracy: Accuracy refers to the proportion of samples that are actually positive among the samples predicted by the model. The calculation formula is: accuracy = number of true cases /(number of true cases + number of false positive cases).
3. Recall rate: Recall rate refers to the proportion of actual positive samples predicted by the model as positive samples. The formula is: recall rate = number of true cases /(number of true cases + number of false negative cases).
4. F1 score: F1 score is the harmonic average of accuracy and recall rate, reflecting the performance of considering both. Formula 1 score =2*(accuracy * recall rate)/(accuracy + recall rate).
